Figures_MSA-PD
Rasmus Rydbirk
04-12-2024
Setup
library(conos)
library(magrittr)
library(dplyr)
library(cacoa) # github.com/kharchenkolab/cacoa## Warning: replacing previous import 'ape::where' by 'dplyr::where' when loading
## 'cacoa'library(sccore)
library(scHelper) # github.com/rrydbirk/scHelper
library(qs)
library(ggplot2)
library(ggsci)
library(cowplot)
library(ggpubr)
library(STRINGdb)
library(reshape2)
library(ggforce)
library(ggpmisc)
library(RColorBrewer)
library(CRMetrics)
library(ggrepel)
library(circlize)
library(ComplexHeatmap)
library(stringr)
library(rstatix)
library(slingshot)
library(gamm4)
library(scITD)
library(ggraph)
## Comparisons
comp <- list(c("CTRL","MSA"),
c("CTRL","PD"),
c("MSA","PD"))
comp.long <- list(c("CTRL vs. MSA","CTRL vs. PD"),
c("CTRL vs. MSA","PD vs. MSA"),
c("CTRL vs. PD","PD vs. MSA"))
# Palettes
## Major celltypes
anno.neurons <- qread("anno_neurons.qs")
anno.neurons.major <- anno.neurons %>%
collapseAnnotation("GABA") %>%
collapseAnnotation("GLU") %>%
renameAnnotation("GABA", "Inh. neurons") %>%
renameAnnotation("GLU", "Exc. neurons") %>%
collapseAnnotation("MSN")## Collapsing 11 labels containing 'GABA' in their name into one label.
## Collapsing 2 labels containing 'GLU' in their name into one label.
## Collapsing 3 labels containing 'MSN' in their name into one label.anno <- qread("anno_major.qs")
anno.major <- anno[!names(anno) %in% names(anno.neurons.major) & !anno == "Neurons"] %>%
{factor(c(., anno.neurons.major))}
pal.major <- brewer.pal(n = 10, "Set1") %>%
c("lightblue3") %>%
setNames(levels(anno.major)[c(9,8,3,5,10,6:7,2,1,4)])## Warning in brewer.pal(n = 10, "Set1"): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Condition
pal.major %<>% c(c("yellow", brewer.pal(n = 6, "Accent")[5:6]) %>% setNames(c("CTRL","PD","MSA")))
## Comparisons
pal.major %<>% c(brewer.pal(n = 10, "Paired")[c(6,8,10)] %>% setNames(c("CTRL vs. MSA","CTRL vs. PD","PD vs. MSA")))
## Neurons
pal.major %<>% c(setNames(c("navy",
"mediumblue",
"lightslateblue",
"lightskyblue",
"lightblue",
"lightseagreen",
"blue",
"cadetblue1",
"cyan",
"cyan4",
"aquamarine2",
"lightcoral",
"brown1",
"orange",
"darkorange4",
"lightgoldenrod3"),
levels(anno.neurons)))
## Glia
anno.glia <- c("anno_astro.qs",
"anno_oligo.qs",
"anno_opc.qs") %>%
lapply(qread) %>%
Reduce(c, .) %>%
factor() %>%
renameAnnotation("Homeostatic_astrocytes", "AS_homeostatic") %>%
renameAnnotation("Reactive_astrocytes", "AS_reactive") %>%
renameAnnotation("Homeostatic_LINC01608", "OL_LINC01608") %>%
renameAnnotation("Homeostatic_SLC5A11", "OL_SLC5A11") %>%
renameAnnotation("Reactive_SCGZ", "OL_SGCZ")
pal.major %<>% c(setNames(c("pink",
pal.major["Astrocytes"],
pal.major["Oligodendrocytes"],
"chartreuse",
"darkolivegreen",
"brown4",
"coral"),
levels(anno.glia)))
## Microglia
anno.micro <- qread("anno_micro_pvm.qs") %>%
renameAnnotation("Steady-state","MIC_steady-state") %>%
renameAnnotation("Intermediate1","MIC_intermediate1") %>%
renameAnnotation("Intermediate2","MIC_intermediate2") %>%
renameAnnotation("Activated","MIC_activated")
pal.major %<>% c(setNames(c(pal.major["Microglia"],
"maroon4",
"magenta",
"pink3",
pal.major["PVMs"]),
levels(anno.micro)))
## Phago. assay
pal.major %<>% c(setNames(c("purple",
"black",
pal.major[c("CTRL","PD","MSA")]),
c("PBS",
"LPS",
"CTRL CSF",
"PD CSF",
"MSA CSF")))
## Houston assay
pal.major %<>% c(setNames(c(pal.major["PBS"],
pal.major["CTRL"],
"yellow3",
"orange",
pal.major["PD"],
"cyan4",
"navyblue",
pal.major["MSA"],
"pink3",
"red4"),
c("Microglia medium",
"CTRL CSF, no dil.",
"CTRL CSF, 1:2 dil.",
"CTRL CSF, 1:4 dil.",
"PD CSF, no dil.",
"PD CSF, 1:2 dil.",
"PD CSF, 1:4 dil.",
"MSA CSF, no dil.",
"MSA CSF, 1:2 dil.",
"MSA CSF, 1:4 dil.")))
# Load major Conos object
con <- qread("con_major.qs", nthreads = 10)
tt <- Sys.time()Define helper functions
getOntWithFamily <- function(cao, comp, name = "GSEA") {
fams <- cao$test.results[[name]]$families
df.all <- list(cao$.__enclos_env__$private$getOntologyPvalueResults(name = name, genes = "up", p.adj = 1, q.value = 1),
cao$.__enclos_env__$private$getOntologyPvalueResults(name = name, genes = "down", p.adj = 1, q.value = 1)) %>%
bind_rows() %>%
mutate(logp = -log10(p.adjust),
comp = comp)
df <- cacoa:::getOntologyFamilyChildren(df.all, fams=fams, type = name, subtype = "BP")
return(list(df.all = df.all,
df = df,
fams = fams))
}lianaCircos <- function(df,
top.interactions = 30,
text.size = 1,
pal = pal.major,
cell.types = c("Astrocytes",
"Immune",
"Microglia",
"Neurons",
"OPCs",
"Oligodendrocytes",
"PVMs",
"Pericytes/endothelial"),
big.gap = 5,
small.gap = 2,
arrow.width = 3,
link.ramp.rel = T,
link.sort = F,
scale = F,
arrow.head.width = 0.3,
arrow.head.length = 0.3,
link.ramp.col = c("navy", "grey", "firebrick")) {
input_df <- df %>%
slice_max(order_by = score, n = top.interactions) %>%
mutate(target = paste0(target, " ")) %>%
mutate(source_lig = paste0(source, "|", ligand),
target_rec = paste0(target, "|", receptor))
if (link.ramp.rel) {
arr_wd <- rep(arrow.width, nrow(input_df))
} else {
arr_wd <- (((input_df$score - min(input_df$score))/(max(input_df$score) - min(input_df$score))) * (arrow.width)) + 1
}
# Colors and segments
anno.col <- setNames(pal,
cell.types) %>%
c(., "Oligodendrocytes " = unname(.["Oligodendrocytes"]))
cell_cols <- anno.col[unique(c(unique(input_df$source), unique(input_df$target), "Oligodendrocytes "))]
link_cols <- c()
if (!link.ramp.rel) {
for (i in input_df$source_lig) {
link_cols <- c(link_cols, cell_cols[str_extract(i,
"[^|]+")])
}
} else {
input_df %<>%
arrange(score)
df.down <- input_df %>% filter(score <= 0)
link_down <- colorRampPalette(c(link.ramp.col[1], link.ramp.col[2]))(nrow(df.down))
df.up <- input_df %>% filter(score > 0)
link_up <- colorRampPalette(c(link.ramp.col[2], link.ramp.col[3]))(nrow(df.up))
link_cols <- c(link_down, link_up)
}
segments <- unique(c(paste0(input_df$source, "|", input_df$ligand),
paste0(input_df$target, "|", input_df$receptor)))
grp <- str_extract(segments, "[^|]+") %>%
setNames(segments)
# Redo colors
cell_cols2 <- grp
for (i in unique(grp)) {
cell_cols2[cell_cols2 == i] <- cell_cols[i]
}
# Plot
input_df %>%
select(source_lig, target_rec, score) %>%
chordDiagram(directional = 1,
group = grp,
scale = scale,
diffHeight = 0.005,
direction.type = c("arrows"),
link.arr.type = "triangle",
annotationTrack = c(),
preAllocateTracks = list(
list(track.height = 0.05),
list(track.height = 0.25),
list(track.height = 0.05)),
big.gap = big.gap,
transparency = 1,
link.arr.lwd = arr_wd,
link.arr.col = link_cols,
link.arr.length = arrow.head.length,
link.arr.width = arrow.head.width,
small.gap = small.gap
)
circos.track(track.index = 2, panel.fun = function(x, y) {
circos.text(CELL_META$xcenter,
CELL_META$ylim[1],
str_extract(CELL_META$sector.index, "[^|]+$"),
facing = "clockwise",
niceFacing = TRUE,
adj = c(0, 0.55),
cex = 1)
}, bg.border = NA)
# Split segments
for (l in segments) {
highlight.sector(l, track.index = 3, col = cell_cols2[l])
}
# Add ligand/receptor track
## Ligand
highlight.sector(input_df$source_lig,
track.index = 1,
col = "black",
text = "Ligands",
cex = 1,
text.col = "white",
niceFacing = TRUE)
## Receptor
highlight.sector(input_df$target_rec,
track.index = 1,
col = "white",
text = "Receptors",
cex = 1,
text.col = "black",
border = "black",
niceFacing = TRUE)
# Legends
minmax <- input_df %>%
pull(score) %>%
{pmax(abs(min(.)), max(.))} %>%
formatC(digits = 1) %>%
as.numeric()
col.range = c(-minmax, 0, minmax)
lgd_links = Legend(at = col.range,
col_fun = colorRamp2(col.range, link.ramp.col),
title_position = "topleft",
title = "Links")
lgd_ct <- Legend(labels = unique(c(input_df$source, input_df$target)),
title = "Cell type",
type = "points",
legend_gp = gpar(col = "transparent"),
background = cell_cols[unique(c(input_df$source, input_df$target))])
lgd_list_vertical = packLegend(lgd_ct, lgd_links)
draw(lgd_list_vertical,
just = c("left", "bottom"),
x = unit(5, "mm"),
y = unit(5, "mm"))
circos.clear()
}getTscanTrajectory <- function(con, anno) {
requireNamespace("TSCAN", quietly = T)
emb <- con$embedding[names(anno), ]
anno %<>% .[rownames(emb)]
cent.ids <- emb %>%
rownames() %>%
split(anno)
centroids <- cent.ids %>%
lapply(\(cid) emb[cid, ]) %>%
lapply(colMeans) %>%
bind_rows() %>%
t() %>%
`colnames<-`(c("UMAP1","UMAP2"))
mst <- centroids %>%
TSCAN::createClusterMST(clusters = NULL)
line.data <- TSCAN::reportEdges(centroids, mst = mst, clusters = NULL)
return(line.data)
}plotGenePseudoBulk <- function(gene, cm.pseudo, legend = T) {
idx <- cm.pseudo %>%
colnames() %>%
data.frame(id = .) %>%
mutate(condition = strsplit(id, "_|!!") %>% sget(1),
ct = strsplit(id, "!!") %>% sget(2)) %>%
mutate(ord = order(condition, ct))
x <- cm.pseudo %>%
.[match(gene, rownames(.)), match(colnames(na.omit(.)), colnames(.))] %>%
.[idx$ord]
plot.dat <- x %>%
{data.frame(sample = names(.),
value = unname(.))} %>%
mutate(anno = strsplit(sample, "!!") %>%
sget(2),
condition = strsplit(sample, "!!|_") %>%
sget(1)) %>%
mutate(anno = factor(anno))
stat.test <- plot.dat %>%
group_by(anno) %>%
rstatix::wilcox_test(value ~ condition) %>%
filter(p.adj <= 0.05) %>%
rstatix::add_xy_position(x = "anno", step.increase = 0.05)
omnibus.test <- plot.dat %>%
group_by(anno) %>%
rstatix::kruskal_test(value ~ condition) %>%
filter(p <= 0.05) %>%
mutate(p = formatC(p, digits = 2))
p <- plot.dat %>%
ggplot(aes(anno, value)) +
geom_boxplot(aes(fill = condition)) +
theme_bw() +
theme(line = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
axis.ticks.x = element_line()) +
labs(x = "", y = "Normalized pseudobulk expression", fill = "", title = paste0(gene, " expression")) +
scale_fill_manual(values = pal.major) +
geom_text(data = omnibus.test, aes(anno, y = pmin(max(plot.dat$value) * 1.05, max(plot.dat$value) + 10), label = p), size = 3) +
stat_pvalue_manual(stat.test, hide.ns = T, label = "p.adj", size = 3)
if (!legend) p <- p + guides(fill = "none")
return(p)
}Figure 1
Load data
cao_msa <- qread("cao_major_msa.qs", nthreads = 10)
cao_msa$plot.theme <- theme_bw()
cao_pd <- qread("cao_major_pd.qs", nthreads = 10)
cao_pd$plot.theme <- theme_bw()
cao_dis <- qread("cao_major_dis.qs", nthreads = 10)
cao_dis$plot.theme <- theme_bw()Figure 1b
con$plotGraph(groups = anno.major,
embedding = "UMAP_1_0.001_5",
size = 0.1,
palette = pal.major,
font.size = 3,
raster = T,
show.labels = T,
plot.na = F,
show.legend = T,
legend.title = "Cell type",
alpha = 0.05) +
dotSize(3) +
labs(x="UMAP1", y= "UMAP2") +
theme(line = element_blank()) +
ylim(c(-20, 15)) +
xlim(c(-21, 11)) -> p
p
Export source data
p$data %>%
write.table("source_data/F1b.tsv", sep = "\t", dec = ".", row.names = F)Figure 1c
cm.merged <- con$getJointCountMatrix()
markers <- c("AQP4","PTPRC","CSF1R","RBFOX3","SLC17A7","GAD1","PPP1R1B","MOG","VCAN","PDGFRB","MRC1")
dotPlot(markers,
cm.merged,
anno.major %>%
factor(., levels = sort(levels(.))[c(1,3,5,2,4,6,7:10)]),
cols = c("white","firebrick"),
gene.order = markers) -> p## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`.
## ℹ See also `vignette("ggplot2-in-packages")` for more information.
## ℹ The deprecated feature was likely used in the sccore package.
## Please report the issue at <https://github.com/kharchenkolab/sccore/issues>.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.p
Export source data
p$data %>%
write.table("source_data/F1c.tsv", sep = "\t", dec = ".", row.names = F)Figure 1d
spc <- con$getDatasetPerCell()
cpc <- spc %>%
as.character() %>%
grepl.replace(c("CTRL","MSA","PD")) %>%
`names<-`(names(spc))
con$plotGraph(groups = cpc,
embedding = "UMAP_1_0.001_5",
size = 0.1,
alpha = 0.05,
palette = pal.major,
font.size = 3,
raster = F,
show.labels = T,
plot.na = F,
mark.groups = F,
show.legend = T) +
labs(x="UMAP1", y= "UMAP2", col = "") +
theme(legend.position = "bottom",
line = element_blank()) +
dotSize(3) -> p
p
Export source data
p$data %>%
write.table("source_data/F1d.tsv", sep = "\t", dec = ".", row.names = F)Figure 1e
cao_msa$plotCellLoadings(show.pvals = F,
alpha = 0)$data %>%
ggplot(aes(ind, values, fill = ind)) +
geom_hline(yintercept = 0, col = "black") +
geom_violin() +
coord_flip() +
theme_bw() +
scale_fill_manual(values = pal.major) +
theme(legend.position = "none",
line = element_blank()) +
scale_y_continuous(breaks = c(-1,0,1),
limits = c(-1,1),
labels = c("-1\nCTRL",0,"1\nMSA")) +
labs(x = "", y = "") -> p
p
Export source data
p$data %>%
write.table("source_data/F1e.tsv", sep = "\t", dec = ".", row.names = F)Figure 1f
cao_pd$plotCellLoadings(show.pvals = F,
alpha = 0)$data %>%
ggplot(aes(ind, values, fill = ind)) +
geom_hline(yintercept = 0, col = "black") +
geom_violin() +
coord_flip() +
theme_bw() +
scale_fill_manual(values = pal.major) +
theme(legend.position = "none",
line = element_blank()) +
scale_y_continuous(breaks = c(-1,0,1),
limits = c(-1,1.1),
labels = c("-1\nCTRL",0,"1\nPD")) +
geom_vline(xintercept = 7.5, col = "red") +
labs(x = "", y = "") -> p
p
Export source data
p$data %>%
write.table("source_data/F1f.tsv", sep = "\t", dec = ".", row.names = F)Figure 1g
cao_dis$plotCellLoadings(show.pvals = F,
alpha = 0)$data %>%
ggplot(aes(ind, values, fill = ind)) +
geom_hline(yintercept = 0, col = "black") +
geom_violin() +
coord_flip() +
theme_bw() +
scale_fill_manual(values = pal.major) +
theme(legend.position = "none",
line = element_blank()) +
scale_y_continuous(breaks = c(-1,0,1),
limits = c(-1,1),
labels = c("-1\nPD",0,"1\nMSA")) +
geom_vline(xintercept = 5.5, col = "red") +
labs(x = "", y = "") -> p
p
Export source data
p$data %>%
write.table("source_data/F1g.tsv", sep = "\t", dec = ".", row.names = F)Figure 1h
cao_msa$cell.groups.palette <- pal.major
stat.p <- cao_msa$test.results$expression.shifts$padjust %>%
{data.frame(Type = names(.), padj = unname(.), value = 0.4)} %>%
mutate(padj = formatC(padj, digits = 3))
stat.p$padj[stat.p$padj > 0.05] <- ""
cao_msa$plotExpressionShiftMagnitudes(show.pvalues = "none") +
labs(y = "Normalized expression distance") +
geom_text(data = stat.p, aes(label = padj)) -> p
p
Export source data
p$data %>%
write.table("source_data/F1h.tsv", sep = "\t", dec = ".", row.names = F)Figure 1i
cao_pd$cell.groups.palette <- pal.major
stat.p <- cao_pd$test.results$expression.shifts$padjust %>%
{data.frame(Type = names(.), padj = unname(.), value = 0.35)} %>%
mutate(padj = formatC(padj, digits = 3))
stat.p$padj[stat.p$padj > 0.05] <- ""
cao_pd$plotExpressionShiftMagnitudes(show.pvalues = "none") +
labs(y = "Normalized expression distance") +
geom_text(data = stat.p, aes(label = padj)) -> p
p
Export source data
p$data %>%
write.table("source_data/F1i.tsv", sep = "\t", dec = ".", row.names = F)Figure 1j
cao_dis$cell.groups.palette <- pal.major
stat.p <- cao_dis$test.results$expression.shifts$padjust %>%
{data.frame(Type = names(.), padj = unname(.), value = 0.3)} %>%
mutate(padj = formatC(padj, digits = 3))
stat.p$padj[stat.p$padj > 0.05] <- ""
cao_dis$plotExpressionShiftMagnitudes(show.pvalues = "none") +
labs(y = "Normalized expression distance") +
geom_text(data = stat.p, aes(label = padj)) -> p
p## Notch went outside hinges
## ℹ Do you want `notch = FALSE`?
Export source data
p$data %>%
write.table("source_data/F1j.tsv", sep = "\t", dec = ".", row.names = F)Figure 2
Load data
cao <- qread("cao_neurons.qs", nthreads = 10)
cao$plot.theme <- theme_bw()
cao_msa <- qread("cao_neurons_msa.qs", nthreads = 10)
cao_msa$plot.theme <- theme_bw()
cao_pd <- qread("cao_neurons_pd.qs", nthreads = 10)
cao_pd$plot.theme <- theme_bw()
cao_dis <- qread("cao_neurons_dis.qs", nthreads = 10)
cao_dis$plot.theme <- theme_bw()Figure 2a
cao$plotEmbedding(groups = cao$cell.groups,
size = 0.1,
palette = pal.major,
font.size = 3,
raster = T,
show.labels = T,
plot.na = F,
alpha = 0.1) +
theme(line = element_blank()) +
labs(x="largeVis1", y= "largeVis2") -> p
p
Export source data
p$data %>%
write.table("source_data/F2a.tsv", sep = "\t", dec = ".", row.names = F)Figure 2b
cm.merged <- cao$data.object$getJointCountMatrix()
markers <- c("GAD1","GAD2","PPP1R1B","SLC17A7","CHAT","CALB2","DCC","ID2","MEIS2","ST18","NKX2-1","NR2F2","PAX6","PVALB","RXFP1","SST","VIP","RELN","SATB2","DRD1","DRD2","FOXP2", "LRP8", "VLDLR")
dotPlot(markers,
cm.merged,
anno.neurons,
cols = c("white","firebrick"),
gene.order = markers) -> p
p
Export source data
p$data %>%
write.table("source_data/F2b.tsv", sep = "\t", dec = ".", row.names = F)Figure 2c
cao_msa$plotCellLoadings(show.pvals = F,
alpha = 0)$data %>%
ggplot(aes(ind, values, fill = ind)) +
geom_hline(yintercept = 0, col = "black") +
geom_violin() +
coord_flip() +
theme_bw() +
scale_fill_manual(values = pal.major) +
theme(legend.position = "none",
line = element_blank()) +
scale_y_continuous(breaks = c(-1,0,1),
limits = c(-1,1),
labels = c("-1\nCTRL",0,"1\nMSA")) +
labs(x = "", y = "") -> p
p
Export source data
p$data %>%
write.table("source_data/F2c.tsv", sep = "\t", dec = ".", row.names = F)Figure 2d
cao_pd$plotCellLoadings(show.pvals = F,
alpha = 0)$data %>%
ggplot(aes(ind, values, fill = ind)) +
geom_hline(yintercept = 0, col = "black") +
geom_violin() +
coord_flip() +
theme_bw() +
scale_fill_manual(values = pal.major) +
theme(legend.position = "none",
line = element_blank()) +
scale_y_continuous(breaks = c(-1,0,1),
limits = c(-1,1),
labels = c("-1\nCTRL",0,"1\nPD")) +
geom_vline(xintercept = 13.5, col = "red") +
labs(x = "", y = "") -> p
p
Export source data
p$data %>%
write.table("source_data/F2d.tsv", sep = "\t", dec = ".", row.names = F)Figure 2e
cao_dis$plotCellLoadings(show.pvals = F,
alpha = 0)$data %>%
ggplot(aes(ind, values, fill = ind)) +
geom_hline(yintercept = 0, col = "black") +
geom_violin() +
coord_flip() +
theme_bw() +
scale_fill_manual(values = pal.major) +
theme(legend.position = "none",
line = element_blank()) +
scale_y_continuous(breaks = c(-1,0,1),
limits = c(-1,1),
labels = c("-1\nPD",0,"1\nMSA")) +
labs(x = "", y = "") -> p
p
Export source data
p$data %>%
write.table("source_data/F2e.tsv", sep = "\t", dec = ".", row.names = F)Figure 2f
cao_msa$cell.groups.palette <- pal.major
cao_msa$plotExpressionShiftMagnitudes() +
labs(y = "Normalized expression distance") -> p1
cao_pd$cell.groups.palette <- pal.major
cao_pd$plotExpressionShiftMagnitudes() +
labs(y = "Normalized expression distance") -> p2
cao_dis$cell.groups.palette <- pal.major
cao_dis$plotExpressionShiftMagnitudes() +
labs(y = "Normalized expression distance") -> p3
p1## Notch went outside hinges
## ℹ Do you want `notch = FALSE`?
p2## Notch went outside hinges
## ℹ Do you want `notch = FALSE`?
## Notch went outside hinges
## ℹ Do you want `notch = FALSE`?
p3## Notch went outside hinges
## ℹ Do you want `notch = FALSE`?
## Notch went outside hinges
## ℹ Do you want `notch = FALSE`?
## Notch went outside hinges
## ℹ Do you want `notch = FALSE`?
## Notch went outside hinges
## ℹ Do you want `notch = FALSE`?
## Notch went outside hinges
## ℹ Do you want `notch = FALSE`?
Export source data
p1$data %>%
write.table("source_data/F2f1.tsv", sep = "\t", dec = ".", row.names = F)
p2$data %>%
write.table("source_data/F2f2.tsv", sep = "\t", dec = ".", row.names = F)
p3$data %>%
write.table("source_data/F2f3.tsv", sep = "\t", dec = ".", row.names = F)Figure 2g
sample.groups <- cao$sample.groups
cao$estimateCellDensity(method = "graph")
cao$estimateDiffCellDensity(type = "subtract")
cao$plotCellDensity(show.cell.groups = F, show.legend = F, color.range = c(0, 0.00035))$CTRL +
geom_circle(aes(x0 = 30.9, y0 = 10, r = 10)) +
geom_circle(aes(x0 = 17.5, y0 = 24, r = 4), col = "cyan3") +
theme(line = element_blank()) -> p
p
Export source data
p$data %>%
write.table("source_data/F2g1.tsv", sep = "\t", dec = ".", row.names = F)# MSA
cao$sample.groups <- sample.groups %>%
names() %>%
grepl.replace(c("CTRL","MSA","PD")) %>%
`names<-`(sample.groups %>% names()) %>%
.[. %in% c("CTRL","MSA")]
cao$target.level <- "MSA"
cao$estimateCellDensity(method = "graph", name = "msa.density")
cao$estimateDiffCellDensity(type = "subtract", name = "msa.density")
cao$plotCellDensity(show.cell.groups = F, name = "msa.density", show.legend = F, color.range = c(0, 0.00035))$MSA +
geom_circle(aes(x0 = 30.9, y0 = 10, r = 10)) +
geom_circle(aes(x0 = 17.5, y0 = 24, r = 4), col = "cyan3") +
theme(line = element_blank()) -> p
p
Export source data
p$data %>%
write.table("source_data/F2g2.tsv", sep = "\t", dec = ".", row.names = F)# PD
cao$sample.groups <- sample.groups %>%
names() %>%
grepl.replace(c("CTRL","MSA","PD")) %>%
`names<-`(sample.groups %>% names()) %>%
.[. %in% c("CTRL","PD")]
cao$target.level <- "PD"
cao$estimateCellDensity(method = "graph", name = "pd.density")
cao$estimateDiffCellDensity(type = "subtract", name = "pd.density")
cao$plotCellDensity(show.cell.groups = F, name = "pd.density", show.legend = T, color.range = c(0, 0.00035))$PD +
geom_circle(aes(x0 = 30.9, y0 = 10, r = 10)) +
geom_circle(aes(x0 = 17.5, y0 = 24, r = 4), col = "cyan3") +
theme(line = element_blank()) -> p
p
Export source data
p$data %>%
write.table("source_data/F2g3.tsv", sep = "\t", dec = ".", row.names = F)Figure 3
Load data
con.glia <- qread("con_oligo_astro_opc.qs", nthreads = 10)
cao_msa <- qread("cao_oligo_astro_opc_msa.qs", nthreads = 10)
cao_msa$plot.theme <- theme_bw()
cao_pd <- qread("cao_oligo_astro_opc_pd.qs", nthreads = 10)
cao_pd$plot.theme <- theme_bw()
cao_dis <- qread("cao_oligo_astro_opc_dis.qs", nthreads = 10)
cao_dis$plot.theme <- theme_bw()Figure 3a
con.glia$plotGraph(groups = anno.glia,
plot.na = F,
size = 0.1,
palette = pal.major,
font.size = 3,
raster = T,
show.labels = T, embedding = "largeVis_CPCA_AS01",
alpha = 0.05) +
labs(x = "largeVis1", y = "largeVis2") +
theme(line = element_blank()) -> p
p
Export source data
p$data %>%
write.table("source_data/F3a.tsv", sep = "\t", dec = ".", row.names = F)Figure 3b
cm.merged <- con.glia$getJointCountMatrix()
markers <- c("AQP4","TNC","MOG","LINC01608","SLC5A11","SGCZ","VCAN","OLIG2")
dotPlot(markers,
cm.merged,
anno.glia,
cols = c("white","firebrick"),
gene.order = markers) -> p
p
Export source data
p$data %>%
write.table("source_data/F3b.tsv", sep = "\t", dec = ".", row.names = F)Figure 3c
cao_msa$plotCellLoadings(show.pvals = F,
alpha = 0)$data %>%
ggplot(aes(ind, values, fill = ind)) +
geom_hline(yintercept = 0, col = "black") +
geom_violin() +
coord_flip() +
theme_bw() +
scale_fill_manual(values = pal.major) +
theme(legend.position = "none",
line = element_blank()) +
scale_y_continuous(breaks = c(-1,0,1),
limits = c(-1.1,1),
labels = c("-1\nCTRL",0,"1\nMSA")) +
geom_vline(xintercept = 6.5, col = "red") +
labs(x = "", y = "") -> p
p
Export source data
p$data %>%
write.table("source_data/F3c.tsv", sep = "\t", dec = ".", row.names = F)Figure 3d
cao_pd$plotCellLoadings(show.pvals = F,
alpha = 0)$data %>%
ggplot(aes(ind, values, fill = ind)) +
geom_hline(yintercept = 0, col = "black") +
geom_violin() +
coord_flip() +
theme_bw() +
scale_fill_manual(values = pal.major) +
theme(legend.position = "none",
line = element_blank()) +
scale_y_continuous(breaks = c(-1,0,1),
limits = c(-1.15,1),
labels = c("-1\nCTRL",0,"1\nPD")) +
geom_vline(xintercept = 4.5, col = "red") +
labs(x = "", y = "") -> p
p
Export source data
p$data %>%
write.table("source_data/F3d.tsv", sep = "\t", dec = ".", row.names = F)Figure 3e
cao_dis$plotCellLoadings(show.pvals = F,
alpha = 0)$data %>%
ggplot(aes(ind, values, fill = ind)) +
geom_hline(yintercept = 0, col = "black") +
geom_violin() +
coord_flip() +
theme_bw() +
scale_fill_manual(values = pal.major) +
theme(legend.position = "none",
line = element_blank()) +
scale_y_continuous(breaks = c(-1,0,1),
limits = c(-1.1,1),
labels = c("-1\nPD",0,"1\nMSA")) +
geom_vline(xintercept = 6.5, col = "red") +
labs(x = "", y = "") -> p
p
Export source data
p$data %>%
write.table("source_data/F3e.tsv", sep = "\t", dec = ".", row.names = F)Figure 3f
stat.p <- cao_msa$test.results$expression.shifts$padjust %>%
{data.frame(Type = names(.), padj = unname(.), value = 0.1)} %>%
mutate(padj = formatC(padj, digits = 3))
stat.p$padj[stat.p$padj > 0.05] <- ""
cao_msa$plotExpressionShiftMagnitudes(show.pvalues = "none") +
labs(y = "Normalized expression distance") +
geom_text(data = stat.p, aes(label = padj)) -> p1
stat.p <- cao_pd$test.results$expression.shifts$padjust %>%
{data.frame(Type = names(.), padj = unname(.), value = 0.14)} %>%
mutate(padj = formatC(padj, digits = 3))
stat.p$padj[stat.p$padj > 0.05] <- ""
cao_pd$plotExpressionShiftMagnitudes(show.pvalues = "none") +
labs(y = "Normalized expression distance") +
geom_text(data = stat.p, aes(label = padj)) -> p2
stat.p <- cao_dis$test.results$expression.shifts$padjust %>%
{data.frame(Type = names(.), padj = unname(.), value = 0.13)} %>%
mutate(padj = formatC(padj, digits = 3))
stat.p$padj[stat.p$padj > 0.05] <- ""
cao_dis$plotExpressionShiftMagnitudes(show.pvalues = "none") +
labs(y = "Normalized expression distance") +
geom_text(data = stat.p, aes(label = padj)) -> p3
p1
p2
p3
Export source data
p1$data %>%
write.table("source_data/F3f1.tsv", sep = "\t", dec = ".", row.names = F)
p2$data %>%
write.table("source_data/F3f2.tsv", sep = "\t", dec = ".", row.names = F)
p3$data %>%
write.table("source_data/F3f3.tsv", sep = "\t", dec = ".", row.names = F)Export source data
p$data %>%
write.table("source_data/F1j.tsv", sep = "\t", dec = ".", row.names = F)## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 11488150 613.6 19635608 1048.7 19635608 1048.7
## Vcells 3018272998 23027.6 6329956030 48293.8 6237672537 47589.7Figure 4
Figure 4a,b
cao_msa <- qread("cao_astro_msa.qs", nthreads = 10)
cao_msa$plot.theme <- theme_bw()
cao_pd <- qread("cao_astro_pd.qs", nthreads = 10)
cao_pd$plot.theme <- theme_bw()
cao_dis <- qread("cao_astro_dis.qs", nthreads = 10)
cao_dis$plot.theme <- theme_bw()
df.all <- Map(getOntWithFamily, list(cao_msa, cao_pd, cao_dis), c("CTRL vs. MSA", "CTRL vs. PD", "PD vs. MSA")) %>%
lget("df.all") %>%
bind_rows() %>%
mutate(Group = paste0("AS_", strsplit(Group, "_") %>% sget(1) %>% tolower()))## Loading required namespace: DOSE## # Select relevant pathways
p.ids <- c("GO:0050821", "GO:0050728", "GO:0006986", "GO:0030168", "GO:0030001", "GO:1904707", "GO:0140053", "GO:0006486", "GO:0010717", "GO:0031113", "GO:0032675", "GO:0061041", "GO:0071222", "GO:0045766", "GO:0006487", "GO:0097501", "GO:0042776", "GO:0045047", "GO:0044794", "GO:0035633", "GO:0097242", "GO:0006120", "GO:0042026", "GO:0006979", "GO:0071222", "GO:0017015", "GO:0009611", "GO:1901201", "GO:0042594", "GO:0097501", "GO:0006123", "GO:0006909", "GO:0007179", "GO:0006120", "GO:0097501")Figure 4a
ct <- "AS_homeostatic"
df.sel <- df.all %>%
filter(Group == ct)
p1 <- ggplot(df.sel, aes(NES, logp, col = comp, shape = comp)) +
geom_point(data = ~filter(., p.adjust > 0.05), size = 0.1, alpha = 0.5, col = "black") +
geom_point(data = ~filter(., p.adjust <= 0.05), size = 2, alpha = 0.8) +
theme_bw() +
geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
scale_color_manual(values = pal.major) +
theme(line = element_blank()) +
labs(y = "-log10(adj. p)", shape = "Comparison", col = "Comparison") +
dotSize(3)
df <- Map(getOntWithFamily, list(cao_msa, cao_pd, cao_dis), c("CTRL vs. MSA", "CTRL vs. PD", "PD vs. MSA")) %>%
lget("df") %>%
bind_rows() %>%
mutate(Group = paste0("AS_", strsplit(Group, "_") %>% sget(1) %>% tolower())) %>%
filter(Group == ct,
ID %in% p.ids)
p2 <- df %>%
filter(p.adjust <= 0.05, NES > 0) %>%
mutate(Group = factor(Group, levels = sort(unique(Group)))) %>%
select(Description, NES, Group, comp) %>%
as.data.frame() %>%
group_by(comp, Group) %>%
group_split() %>%
lapply(dplyr::slice, seq(5)) %>%
bind_rows() %>%
mutate(., x = 0, y = rev(seq(nrow(.)))) %>%
ggplot(aes(x, y, label = Description, col = comp)) +
geom_text(hjust = 0) +
theme_void() +
xlim(0, 1) +
labs(title = "Selected terms for up-regulated genes") +
scale_color_manual(values = pal.major) +
guides(col = "none")
p3 <- df %>%
filter(p.adjust <= 0.05, NES < 0) %>%
select(Description, NES, Group, comp) %>%
group_by(comp, Group) %>%
group_split() %>%
lapply(dplyr::slice, seq(5)) %>%
bind_rows() %>%
mutate(., x = 0, y = rev(seq(nrow(.)))) %>%
ggplot(aes(x, y, label = Description, col = comp)) +
geom_text(hjust = 0) +
theme_void() +
xlim(0, 1) +
labs(title = "Selected terms for down-regulated genes") +
scale_color_manual(values = pal.major) +
guides(col = "none")
plot_grid(plotlist = list(p3, p1, p2), ncol = 3, rel_widths = c(0.9, 1, 0.9))
Export source data
p1$data %>%
write.table("source_data/F4a.tsv", sep = "\t", dec = ".", row.names = F)Figure 4b
ct <- "AS_reactive"
df.sel <- df.all %>%
filter(Group == ct)
p1 <- ggplot(df.sel, aes(NES, logp, col = comp, shape = comp)) +
geom_point(data = ~filter(., p.adjust > 0.05), size = 0.1, alpha = 0.5, col = "black") +
geom_point(data = ~filter(., p.adjust <= 0.05), size = 2, alpha = 0.8) +
theme_bw() +
geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
scale_color_manual(values = pal.major) +
theme(line = element_blank()) +
labs(y = "-log10(adj. p)", shape = "Comparison", col = "Comparison") +
dotSize(3)
df <- Map(getOntWithFamily, list(cao_msa, cao_pd, cao_dis), c("CTRL vs. MSA", "CTRL vs. PD", "PD vs. MSA")) %>%
lget("df") %>%
bind_rows() %>%
mutate(Group = paste0("AS_", strsplit(Group, "_") %>% sget(1) %>% tolower())) %>%
filter(Group == ct,
ID %in% p.ids)
p2 <- df %>%
filter(p.adjust <= 0.05, NES > 0) %>%
mutate(Group = factor(Group, levels = sort(unique(Group)))) %>%
select(Description, NES, Group, comp) %>%
group_by(comp, Group) %>%
group_split() %>%
lapply(dplyr::slice, seq(5)) %>%
bind_rows() %>%
mutate(., x = 0, y = rev(seq(nrow(.)))) %>%
ggplot(aes(x, y, label = Description, col = comp)) +
geom_text(hjust = 0) +
theme_void() +
xlim(0, 1) +
labs(title = "Selected terms for up-regulated genes") +
scale_color_manual(values = pal.major) +
guides(col = "none")
p3 <- df %>%
filter(p.adjust <= 0.05, NES < 0) %>%
select(Description, NES, Group, comp) %>%
group_by(comp, Group) %>%
group_split() %>%
lapply(dplyr::slice, seq(5)) %>%
bind_rows() %>%
mutate(., x = 0, y = rev(seq(nrow(.)))) %>%
ggplot(aes(x, y, label = Description, col = comp)) +
geom_text(hjust = 0) +
theme_void() +
xlim(0, 1) +
labs(title = "Selected terms for down-regulated genes") +
scale_color_manual(values = pal.major) +
guides(col = "none")
plot_grid(plotlist = list(p3, p1, p2), ncol = 3, rel_widths = c(0.9, 1, 0.9))
p1$data %>%
write.table("source_data/F4b.tsv", sep = "\t", dec = ".", row.names = F)Figure 4c
cao_msa <- qread("cao_oligo_msa.qs", nthreads = 10)
cao_msa$plot.theme <- theme_bw()
cao_pd <- qread("cao_oligo_pd.qs", nthreads = 10)
cao_pd$plot.theme <- theme_bw()
cao_dis <- qread("cao_oligo_dis.qs", nthreads = 10)
cao_dis$plot.theme <- theme_bw()
df.all <- Map(getOntWithFamily, list(cao_msa, cao_pd, cao_dis), c("CTRL vs. MSA", "CTRL vs. PD", "PD vs. MSA")) %>%
lget("df.all") %>%
bind_rows() %>%
mutate(Group = paste0("OL_", strsplit(Group, "_") %>% sget(2) %>% gsub("SCGZ", "SGCZ", .)))
# Select relevant pathways
p.ids <- c("GO:0071345", "GO:0045596", "GO:0060271", "GO:0048709", "GO:0006986", "GO:0016032", "GO:0060271", "GO:0042026", "GO:0006986", "GO:0120034", "GO:0071346", "GO:1904262", "GO:0042776", "GO:0061077", "GO:0050821", "GO:0045047", "GO:0042776", "GO:0050821", "GO:0045047", "GO:0007042", "GO:0042776", "GO:0061077", "GO:0045047", "GO:0042776", "GO:0045047", "GO:2000249", "GO:0002474", "GO:0042026", "GO:0002040", "GO:0046330", "GO:0043542", "GO:0030036", "GO:0032981", "GO:0007042")
ct <- "OL_SLC5A11"
df.sel <- df.all %>%
filter(Group == ct)
p1 <- ggplot(df.sel, aes(NES, logp, col = comp, shape = comp)) +
geom_point(data = ~filter(., p.adjust > 0.05), size = 0.1, alpha = 0.5, col = "black") +
geom_point(data = ~filter(., p.adjust <= 0.05), size = 2, alpha = 0.8) +
theme_bw() +
geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
scale_color_manual(values = pal.major) +
theme(line = element_blank()) +
labs(y = "-log10(adj. p)", shape = "Comparison", col = "Comparison") +
dotSize(3)
df <- Map(getOntWithFamily, list(cao_msa, cao_pd, cao_dis), c("CTRL vs. MSA", "CTRL vs. PD", "PD vs. MSA")) %>%
lget("df") %>%
bind_rows() %>%
mutate(Group = paste0("OL_", strsplit(Group, "_") %>% sget(2) %>% gsub("SCGZ", "SGCZ", .))) %>%
filter(Group == ct,
ID %in% p.ids)
p2 <- df %>%
filter(p.adjust <= 0.05, NES > 0) %>%
mutate(Group = factor(Group, levels = sort(unique(Group)))) %>%
select(Description, NES, Group, comp) %>%
group_by(comp, Group) %>%
group_split() %>%
lapply(dplyr::slice, seq(5)) %>%
bind_rows() %>%
mutate(., x = 0, y = rev(seq(nrow(.)))) %>%
ggplot(aes(x, y, label = Description, col = comp)) +
geom_text(hjust = 0) +
theme_void() +
xlim(0, 1) +
labs(title = "Selected terms for up-regulated genes") +
scale_color_manual(values = pal.major) +
guides(col = "none")
p3 <- df %>%
filter(p.adjust <= 0.05, NES < 0) %>%
select(Description, NES, Group, comp) %>%
group_by(comp, Group) %>%
group_split() %>%
lapply(dplyr::slice, seq(5)) %>%
bind_rows() %>%
mutate(., x = 0, y = rev(seq(nrow(.)))) %>%
ggplot(aes(x, y, label = Description, col = comp)) +
geom_text(hjust = 0) +
theme_void() +
xlim(0, 1) +
labs(title = "Selected terms for down-regulated genes") +
scale_color_manual(values = pal.major) +
guides(col = "none")
plot_grid(plotlist = list(p3, p1, p2), ncol = 3, rel_widths = c(0.9, 1, 0.9))
Export source data
p1$data %>%
write.table("source_data/F4c.tsv", sep = "\t", dec = ".", row.names = F)Figure 4d,e
cm.merged <- con$getJointCountMatrix(raw = T) %>%
Matrix::t() %>%
.[, colnames(.) %in% names(anno.glia)]
# Create sample-wise annotation
anno.donor <- con$getDatasetPerCell()[colnames(cm.merged)]
anno.subtype <- anno.glia %>%
.[!is.na(.)] %>%
factor()
idx <- intersect(anno.donor %>% names(), anno.subtype %>% names())
anno.donor %<>% .[idx]
anno.subtype %<>% .[idx] %>%
{`names<-`(as.character(.), names(.))}
anno.final <- paste0(anno.donor,"!!",anno.subtype) %>%
`names<-`(anno.donor %>% names())
# Create pseudo CM
cm.pseudo <- sccore::collapseCellsByType(cm.merged %>% Matrix::t(),
groups = anno.final, min.cell.count = 1) %>%
t() %>%
apply(2, as, "integer")
cm.pseudo %<>%
{. + 1} %>%
DESeq2::DESeqDataSetFromMatrix(.,
colnames(.) %>%
strsplit("_") %>%
sget(1) %>%
data.frame() %>%
`dimnames<-`(list(colnames(cm.pseudo), "group")),
design = ~ group) %>%
DESeq2::estimateSizeFactors() %>%
DESeq2::counts(normalized = T)## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factorsFigure 4d
# For DE between visits
genes <- "TLR1"
idx <- cm.pseudo %>%
colnames() %>%
data.frame(id = .) %>%
mutate(condition = strsplit(id, "_|!!") %>% sget(1),
ct = strsplit(id, "!!") %>% sget(2)) %>%
mutate(ord = order(condition, ct))
x <- cm.pseudo %>%
.[match(genes, rownames(.)), match(colnames(na.omit(.)), colnames(.))] %>%
.[idx$ord]
plot.dat <- x %>%
{data.frame(sample = names(.),
value = unname(.))} %>%
mutate(anno = strsplit(sample, "!!") %>%
sget(2),
condition = strsplit(sample, "!!|_") %>%
sget(1)) %>%
filter(anno %in% c("OL_LINC01608", "OL_SGCZ", "OL_SLC5A11")) %>%
mutate(anno = factor(anno))
stat.test <- plot.dat %>%
group_by(anno) %>%
rstatix::dunn_test(value ~ condition) %>%
rstatix::add_xy_position(x = "anno", step.increase = 0.1, fun = "mean_sd", scales = "free") %>%
mutate(p.adj = formatC(p.adj, digits = 2))
plot.dat %>%
ggplot(aes(anno, value)) +
geom_boxplot(aes(fill = condition)) +
theme_bw() +
theme(line = element_blank(),
axis.ticks.x = element_line(),
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
labs(x = "", y = "Normalized pseudobulk expression", fill = "", title = "TLR1 expression") +
scale_fill_manual(values = pal.major) +
stat_compare_means(aes(fill = condition), method = "kruskal", label = "p.format", label.y = 8.5e2) +
stat_pvalue_manual(stat.test, label = "p.adj", hide.ns = T) -> p## Warning in stat_compare_means(aes(fill = condition), method = "kruskal", :
## Ignoring unknown aesthetics: fillp
p$data %>%
write.table("source_data/F4d.tsv", sep = "\t", dec = ".", row.names = F)Figure 4e
# For DE between visits
genes <- "LRP1"
idx <- cm.pseudo %>%
colnames() %>%
data.frame(id = .) %>%
mutate(condition = strsplit(id, "_|!!") %>% sget(1),
ct = strsplit(id, "!!") %>% sget(2)) %>%
mutate(ord = order(condition, ct))
x <- cm.pseudo %>%
.[match(genes, rownames(.)), match(colnames(na.omit(.)), colnames(.))] %>%
.[idx$ord]
plot.dat <- x %>%
{data.frame(sample = names(.),
value = unname(.))} %>%
mutate(anno = strsplit(sample, "!!") %>%
sget(2),
condition = strsplit(sample, "!!|_") %>%
sget(1)) %>%
filter(anno %in% c("OL_LINC01608", "OL_SGCZ", "OL_SLC5A11")) %>%
mutate(anno = factor(anno))
stat.test <- plot.dat %>%
group_by(anno) %>%
rstatix::wilcox_test(value ~ condition) %>%
rstatix::add_xy_position(x = "anno", step.increase = 0.05) %>%
mutate(p.adj = formatC(p.adj, digits = 2))
plot.dat %>%
ggplot(aes(anno, value)) +
geom_boxplot(aes(fill = condition)) +
theme_bw() +
theme(line = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
axis.ticks.x = element_line()) +
labs(x = "", y = "Normalized pseudobulk expression", fill = "", title = "LRP1 expression") +
scale_fill_manual(values = pal.major) +
stat_compare_means(aes(fill = condition), method = "kruskal", label = "p.format", label.y = 175) +
stat_pvalue_manual(stat.test, label = "p.adj", hide.ns = T) +
ylim(c(0, 180)) -> p## Warning in stat_compare_means(aes(fill = condition), method = "kruskal", :
## Ignoring unknown aesthetics: fillp
Export source data
p$data %>%
write.table("source_data/F4e.tsv", sep = "\t", dec = ".", row.names = F)Figure 4f
For calculation of data, see Liana.ipynb.
dat <- read.delim("liana_res.csv",
sep = ",",
header = T) %>%
mutate(group = strsplit(sample, "_") %>%
sget(1))
dat.plot <- dat %>%
dplyr::rename(ligand = ligand_complex,
receptor = receptor_complex,
score = lrscore) %>%
filter(target == "Oligodendrocytes",
ligand %in% c("BMP1", "SERPINE2", "PSAP", "APOE", "APP", "SERPING1"),
receptor %in% c("LRP1", "BMPR1A", "LRP4")) %>%
group_by(group, ligand, receptor, source, target) %>%
summarize(score = mean(score)) %>%
ungroup() %>%
arrange(group, source, target, ligand, receptor)## `summarise()` has regrouped the output.
## ℹ Summaries were computed grouped by group, ligand, receptor, source, and
## target.
## ℹ Output is grouped by group, ligand, receptor, and source.
## ℹ Use `summarise(.groups = "drop_last")` to silence this message.
## ℹ Use `summarise(.by = c(group, ligand, receptor, source, target))` for
## per-operation grouping (`?dplyr::dplyr_by`) instead.dat.msa <- dat.plot %>%
filter(group == "MSA",
score > 0.39) %>%
mutate(lrst = paste0(ligand, receptor, source, target))
dat.pd <- dat.plot %>%
mutate(lrst = paste0(ligand, receptor, source, target)) %>%
filter(group == "PD",
lrst %in% dat.msa$lrst)
dat.rel <- dat.msa %>%
mutate(score = score - dat.pd$score)
dat.rel %>%
lianaCircos()
dat.rel %>%
write.table("source_data/F4f.tsv", sep = "\t", dec = ".", row.names = F)Figure 5 & EDF14
Load data
cao <- qread("cao_micro_pvm.qs", nthreads = 10)
cao$plot.theme <- theme_bw()
cao_msa <- qread("cao_micro_pvm_msa.qs", nthreads = 10)
cao_msa$plot.theme <- theme_bw()
cao_pd <- qread("cao_micro_pvm_pd.qs", nthreads = 10)
cao_pd$plot.theme <- theme_bw()
cao_dis <- qread("cao_micro_pvm_dis.qs", nthreads = 10)
cao_dis$plot.theme <- theme_bw()sample.groups <- cao$sample.groupsFigure 5a
cao$plotEmbedding(groups = anno.micro,
size = 0.5,
palette = pal.major,
font.size = 3,
raster = T,
mark.groups = T,
plot.na = F,
alpha = 0.2) +
labs(x="UMAP1", y= "UMAP2") +
theme(line = element_blank()) -> p
p
p$data %>%
write.table("source_data/F5a.tsv", sep = "\t", dec = ".", row.names = F)Figure 5b
cm.merged <- cao$data.object$getJointCountMatrix()
markers <- c("AIF1","F13A1","MRC1","CD163","CD74")
dotPlot(markers,
cm.merged,
cao$cell.groups,
cols = c("white","firebrick")) -> p
p
Export source data
p$data %>%
write.table("source_data/F5b.tsv", sep = "\t", dec = ".", row.names = F)Figure 5c-e
anno.sort <- anno.micro[rownames(cao$data.object$embedding)]
anno.sel <- anno.sort[!anno.sort %in% c("MIC_intermediate1", "PVMs")] %>% factor()
anno.sel2 <- anno.sort[!anno.sort %in% c("MIC_intermediate2", "MIC_activated")] %>% factor()
ldata1 <- getTscanTrajectory(cao$data.object, anno.sel)
ldata2 <- getTscanTrajectory(cao$data.object, anno.sel2)
ldata <- rbind(ldata1, ldata2)Figure 5c
cao$data.object$embedding %>%
as.data.frame() %>%
mutate(., unname(anno.micro[rownames(.)])) %>%
setNames(c("UMAP1", "UMAP2", "annotation")) %>%
ggplot() +
geom_point(aes(UMAP1, UMAP2, col = annotation), size = 0.3) +
geom_line(data = ldata, mapping=aes(UMAP1, UMAP2, group = edge), linewidth = 1) +
theme_bw() +
theme(legend.position = "right",
line = element_blank()) +
labs(col = "", x = "UMAP1", y = "UMAP2") +
scale_colour_manual(values = pal.major) +
dotSize(3) -> p
p
Export source data
n <- nrow(p$data)
cbind(p$data, p@layers$geom_line$data[seq_len(n), ]) %>%
write.table("source_data/F5c.tsv", sep = "\t", dec = ".", row.names = F)Figures 5d,e & EDF14
Prepare data
emb <- cao$data.object$embedding %>%
`colnames<-`(c("UMAP1","UMAP2")) %>%
.[rownames(.) %in% names(anno.sel2), ]
sds_obj <- slingshot(emb,
anno.sel2,
start.clus = "MIC_steady-state",
stretch = 0
)
sds <- as.SlingshotDataSet(sds_obj)
pseudotime <- sds_obj@assays@data@listData$pseudotime[, 1]Here, we show the code for running the generalized additive mixed
model. It takes a significant amount of time to run, we provide data in
pseudotime_micro_l2.qs.
mat <- cao$data.object$getJointCountMatrix() %>%
.[match(names(sds_obj), rownames(.)), colSums(.) > 2e2] %>%
.[, !grepl(pattern = "RPL|RPS|MT-", colnames(.))]
mt <- mat %>%
as.matrix() %>%
as.data.frame() %>%
tibble::rownames_to_column() %>%
{message("Mutating"); mutate(.,
pseudotime = pseudotime[rowname],
condition = cao$data.object$getDatasetPerCell()[rowname] %>%
as.character() %>%
strsplit("_") %>%
sget(1),
sample = strsplit(rowname, "!!") %>%
sget(1))} %>%
.[complete.cases(.), ] %>%
{message("Melting"); reshape2::melt(., id.vars = c("rowname", "pseudotime", "condition", "sample"))} %$%
{message("Splitting"); split(., variable)}res <- mt %>%
sccore::plapply(\(x) {
fit_full <- gamm4(data = x, formula = value ~ s(pseudotime, by = factor(condition)), random = ~(1 | sample), REML = F)
fit_reduced <- gamm4(data = x, formula = value ~ s(pseudotime), random = ~(1 | sample), REML = F)
fit_none <- gamm4(data = x, formula = value ~ 1, random = ~(1 | sample), REML = F)
ann <- anova(fit_full$mer, fit_reduced$mer, fit_none$mer)
if (any(ann$`Pr(>Chisq)` <= 0.05, na.rm = T)) {
residuals <- predict(fit_full$gam, se.fit = T)$fit %>%
unname()
r.sq <- c(summary(fit_full$gam)$r.sq, summary(fit_reduced$gam)$r.sq) %>%
setNames(c("full", "reduced"))
out <- list(anova = ann,
residuals = residuals,
r.sq = r.sq)
return(out)
}
}, n.cores = 40, mc.preschedule = T, mc.cleanup = T, progress = F) %>%
.[!sapply(., is.null)]
qsave(res, "pseudotime_micro_l2.qs")Figure 5d
cao$data.object$embedding %>%
as.data.frame() %>%
mutate(., unname(anno.micro[rownames(.)])) %>%
setNames(c("UMAP1", "UMAP2", "annotation")) %>%
mutate(., pseudotime = pseudotime[rownames(.)]) %>%
ggplot() +
geom_point(aes(UMAP1, UMAP2, col = pseudotime), size = 0.3) +
geom_line(data = ldata2, aes(UMAP1, UMAP2, group = edge), linewidth = 1) +
theme_bw() +
theme(line = element_blank()) +
scale_color_gradient(low = "navyblue", high = "orange") +
labs(col = "Pseudotime", x = "UMAP1", y = "UMAP2") -> p
p
Export source data
n <- nrow(p$data)
cbind(p$data, p@layers$geom_line$data[seq_len(n), ]) %>%
write.table("source_data/F5d.tsv", sep = "\t", dec = ".", row.names = F)Figure 5e
Please note, row order may change per iteration.
res <- qread("pseudotime_micro_l2.qs")
rsq.pseudo <- res %>%
sapply(\(gene) gene$r.sq[1]) %>%
sort(decreasing = T)
res.filter <- res[names(rsq.pseudo)[seq(50)] %>% strsplit(".", fixed = T) %>% sget(1)]
cm.merged <- cao$data.object$getJointCountMatrix() %>%
.[match(names(pseudotime), rownames(.)), colnames(.) %in% names(res.filter)] %>%
.[rowSums(.) > 0, ]
## Predict smoothend expression
pseudotime.both <- pseudotime[rownames(cm.merged)]
weights.both <- sds_obj@assays@data@listData$weights %>% .[match(names(pseudotime.both), rownames(.)), 1] + 1E-7
scFit <- cm.merged %>%
Matrix::t() %>%
tradeSeq::fitGAM(pseudotime = pseudotime.both, cellWeights = weights.both, verbose = T)
Smooth <- tradeSeq::predictSmooth(scFit, gene = colnames(cm.merged), tidy = F, n=100)
# Average across replicates and scale
Smooth <- t(scale(t(Smooth)))
# Seriate the results
Smooth <- Smooth[ seriation::get_order(seriation::seriate(Smooth, method="PCA_angle")), ]
# Create heatmap
col_fun = circlize::colorRamp2(c(-4, 0, 4), c("navy", "white", "firebrick"))
Heatmap(Smooth,
col = col_fun,
cluster_columns=F,
cluster_rows=F,
show_column_names = F,
row_names_gp = grid::gpar(fontsize = 5)) -> p
p
Export source data
p@matrix %>%
write.table("source_data/F5e.tsv", sep = "\t", dec = ".", row.names = F)## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 13803240 737.2 39451381 2107.0 39451381 2107.0
## Vcells 1572541131 11997.6 5833800118 44508.4 9054649287 69081.5Extended Data Figure 14
We provide this figure here since all data are already loaded.
cpc <- pseudotime %>%
names() %>%
strsplit("_") %>%
sget(1) %>%
factor()
res[rownames(Smooth)] %>%
lget("residuals") %>%
lapply(as.data.frame) %>%
lapply(setNames, "residuals") %>%
lapply(mutate, group = cpc, pseudotime = pseudotime) %>%
data.table::rbindlist(idcol = "gene") %>%
ggplot(aes(pseudotime, residuals, col = group)) +
geom_smooth() +
theme_bw() +
facet_wrap(~gene, ncol = 5, scales = "free") -> p
p## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'
Figure 5f
cao_msa$plotCellLoadings(show.pvals = F,
alpha = 0)$data %>%
ggplot(aes(ind, values, fill = ind)) +
geom_hline(yintercept = 0, col = "black") +
geom_violin() +
coord_flip() +
theme_bw() +
scale_fill_manual(values = pal.major) +
theme(legend.position = "none") +
scale_y_continuous(breaks = c(-1,0,1),
limits = c(-1,1),
labels = c("-1\nCTRL",0,"1\nMSA")) +
geom_vline(xintercept = 4.5, col = "red") +
labs(x = "", y = "") +
theme(line = element_blank()) -> p
p
p$data %>%
write.table("source_data/F5f.tsv", sep = "\t", dec = ".", row.names = F)Figure 5g
cao_pd$plotCellLoadings(show.pvals = F,
alpha = 0)$data %>%
ggplot(aes(ind, values, fill = ind)) +
geom_hline(yintercept = 0, col = "black") +
geom_violin() +
coord_flip() +
theme_bw() +
scale_fill_manual(values = pal.major) +
theme(legend.position = "none") +
scale_y_continuous(breaks = c(-1,0,1),
limits = c(-1,1.1),
labels = c("-1\nCTRL",0,"1\nPD")) +
geom_vline(xintercept = 4.5, col = "red") +
labs(x = "", y = "") +
theme(line = element_blank()) -> p
p
Export source data
p$data %>%
write.table("source_data/F5g.tsv", sep = "\t", dec = ".", row.names = F)Figure 5h
cao_dis$plotCellLoadings(show.pvals = F,
alpha = 0)$data %>%
ggplot(aes(ind, values, fill = ind)) +
geom_hline(yintercept = 0, col = "black") +
geom_violin() +
coord_flip() +
theme_bw() +
scale_fill_manual(values = pal.major) +
theme(legend.position = "none") +
scale_y_continuous(breaks = c(-1,0,1),
limits = c(-1.2,1),
labels = c("-1\nPD",0,"1\nMSA")) +
geom_vline(xintercept = 3.5, col = "red") +
labs(x = "", y = "") +
theme(line = element_blank()) -> p
p
p$data %>%
write.table("source_data/F5h.tsv", sep = "\t", dec = ".", row.names = F)## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 13358041 713.4 39451381 2107.0 39451381 2107.0
## Vcells 1427337638 10889.8 4667040095 35606.7 9054649287 69081.5Figure 5j
We provide combined data from the RNAscope experiments as a single
file res.qs.
res <- qread("RNAscope.qs") %>%
filter(target == "AIF1", Ch1NumSpots > 0) %>%
arrange(file)
area <- read.table("RNAscope_areas.tsv", sep = "\t", header = T) %>% # Million pixels
mutate(file = paste(File.name, Tissue, sep = "")) %>%
filter(file %in% res$file) %>%
arrange(file) %>%
mutate(rel_px = px.2 / 1E6)p1 <- res %>%
group_by(group, target, file) %>%
summarize(spots = mean(Ch1NumSpots)) %>%
ggplot(aes(group, spots, fill = group)) +
geom_boxplot() +
geom_jitter(width = 0.2) +
theme_bw() +
labs(y = "Mean no. spots per double-positive cell", x = "") +
stat_compare_means(method = "kruskal.test", label.y = 115) +
stat_compare_means(comparisons = comp, method = "wilcox.test") +
theme(line = element_blank()) +
guides(fill = "none") +
scale_fill_manual(values = pal.major)## `summarise()` has regrouped the output.
## ℹ Summaries were computed grouped by group, target, and file.
## ℹ Output is grouped by group and target.
## ℹ Use `summarise(.groups = "drop_last")` to silence this message.
## ℹ Use `summarise(.by = c(group, target, file))` for per-operation grouping
## (`?dplyr::dplyr_by`) instead.p2 <- res %>%
group_by(group, target, file) %>%
summarize(no_cells = n()) %>%
as.data.frame() %>%
arrange(file) %>%
mutate(rel_cells = no_cells / area$rel_px) %>%
ggplot(aes(group, rel_cells, fill = group)) +
geom_boxplot(outliers = F) +
geom_jitter(width = 0.2) +
theme_bw() +
stat_compare_means(method = "kruskal.test", label.y = 17) +
stat_compare_means(comparisons = comp, method = "wilcox.test") +
labs(y = "No. double-positive cells\nNormalized to area", x = "") +
theme(line = element_blank()) +
guides(fill = "none") +
scale_fill_manual(values = pal.major)## `summarise()` has regrouped the output.
## ℹ Summaries were computed grouped by group, target, and file.
## ℹ Output is grouped by group and target.
## ℹ Use `summarise(.groups = "drop_last")` to silence this message.
## ℹ Use `summarise(.by = c(group, target, file))` for per-operation grouping
## (`?dplyr::dplyr_by`) instead.plot_grid(plotlist = list(p1, p2), ncol = 1)
Export source data
p1$data %>%
write.table("source_data/F5j1.tsv", sep = "\t", dec = ".", row.names = F)
p2$data %>%
write.table("source_data/F5j2.tsv", sep = "\t", dec = ".", row.names = F)Figure 5k
fams <- cao_msa$test.results[["GSEA"]]$families
df.all <- list(cao_msa$.__enclos_env__$private$getOntologyPvalueResults(name = "GSEA", genes = "up", p.adj = 1, q.value = 1),
cao_msa$.__enclos_env__$private$getOntologyPvalueResults(name = "GSEA", genes = "down", p.adj = 1, q.value = 1)) %>%
bind_rows() %>%
mutate(logp = -log10(p.adjust))
# Relevant pathways
p.ids <- c("GO:0060760", "GO:0002230", "GO:0019915", "GO:1904646", "GO:0042775", "GO:0019646", "GO:0033108", "GO:0035455", "GO:0061077", "GO:0006914", "GO:0042026", "GO:0034340", "GO:0051607", "GO:0060337", "GO:0035455", "GO:0002684", "GO:0036037", "GO:0048514")
df <- cacoa:::getOntologyFamilyChildren(df.all, fams=fams, type = "GSEA", subtype = "BP") %>%
mutate(Group = paste0("MIC_", tolower(Group)) %>% gsub("MIC_pvms", "PVMs", .)) %>%
filter(ID %in% p.ids)
df.all %<>% mutate(Group = paste0("MIC_", tolower(Group)) %>% gsub("MIC_pvms", "PVMs", .))
p1 <- ggplot(df.all, aes(NES, logp, col = Group, shape = Group)) +
geom_point(data = ~filter(., p.adjust > 0.05), size = 0.1, alpha = 0.5, col = "black") +
geom_point(data = ~filter(., p.adjust <= 0.05), size = 2, alpha = 0.5) +
theme_bw() +
geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
scale_color_manual(values = pal.major) +
theme(line = element_blank()) +
labs(y = "-log10(adj. p)") +
dotSize(3)
p2 <- df %>%
filter(p.adjust <= 0.05, NES > 0) %>%
mutate(Group = factor(Group, levels = sort(unique(Group)))) %>%
select(Description, NES, Group) %$%
split(., Group) %>%
lapply(dplyr::slice, seq(5)) %>%
bind_rows() %>%
dplyr::slice(seq(25)) %>%
mutate(., x = 0, y = rev(seq(nrow(.)))) %>%
ggplot(aes(x, y, label = Description, col = Group)) +
geom_text(hjust = 0) +
theme_void() +
xlim(0, 1) +
labs(title = "Selected terms for up-regulated genes") +
scale_color_manual(values = pal.major) +
guides(col = "none")
p3 <- df %>%
filter(p.adjust <= 0.05, NES < 0) %>%
select(Description, NES, Group) %>%
dplyr::slice(seq(20)) %>%
mutate(., x = 0, y = rev(seq(nrow(.)))) %>%
ggplot(aes(x, y, label = Description, col = Group)) +
geom_text(hjust = 0) +
theme_void() +
xlim(0, 1) +
labs(title = "Selected terms for down-regulated genes") +
scale_color_manual(values = pal.major) +
guides(col = "none")
plot_grid(plotlist = list(p3, p1, p2), ncol = 3, rel_widths = c(0.9, 1, 0.9))
p1$data %>%
write.table("source_data/F5k.tsv", sep = "\t", dec = ".", row.names = F)## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 12823655 684.9 39451381 2107.0 39451381 2107.0
## Vcells 1283500184 9792.4 3733632076 28485.4 9054649287 69081.5Figure 5l
fams <- cao_pd$test.results[["GSEA"]]$families
df.all <- list(cao_pd$.__enclos_env__$private$getOntologyPvalueResults(name = "GSEA", genes = "up", p.adj = 1, q.value = 1),
cao_pd$.__enclos_env__$private$getOntologyPvalueResults(name = "GSEA", genes = "down", p.adj = 1, q.value = 1)) %>%
bind_rows() %>%
mutate(logp = -log10(p.adjust))
# Select relevant pathways
p.ids <- c("GO:0042776", "GO:0034341", "GO:0001916", "GO:0002503", "GO:0019885", "GO:0016042", "GO:0001909", "GO:0051085", "GO:0045824", "GO:0044000", "GO:0019885", "GO:0044242", "GO:0001944", "GO:0034976", "GO:0019646", "GO:0009205", "GO:0019883", "GO:0070972", "GO:0002474")
df <- cacoa:::getOntologyFamilyChildren(df.all, fams=fams, type = "GSEA", subtype = "BP") %>%
mutate(Group = paste0("MIC_", tolower(Group)) %>% gsub("MIC_pvms", "PVMs", .)) %>%
filter(ID %in% p.ids)
df.all %<>% mutate(Group = paste0("MIC_", tolower(Group)) %>% gsub("MIC_pvms", "PVMs", .))
p1 <- ggplot(df.all, aes(NES, logp, col = Group, shape = Group)) +
geom_point(data = ~filter(., p.adjust > 0.05), size = 0.1, alpha = 0.5, col = "black") +
geom_point(data = ~filter(., p.adjust <= 0.05), size = 2, alpha = 0.5) +
theme_bw() +
geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
scale_color_manual(values = pal.major) +
theme(line = element_blank()) +
labs(y = "-log10(adj. p)") +
dotSize(3)
p2 <- df %>%
filter(p.adjust <= 0.05, NES > 0) %>%
select(Description, NES, Group) %$%
split(., Group) %>%
lapply(dplyr::slice, seq(5)) %>%
bind_rows() %>%
dplyr::slice(seq(25)) %>%
mutate(., x = 0, y = rev(seq(nrow(.)))) %>%
ggplot(aes(x, y, label = Description, col = Group)) +
geom_text(hjust = 0) +
theme_void() +
xlim(0, 1) +
labs(title = "Selected terms for up-regulated genes") +
scale_color_manual(values = pal.major) +
guides(col = "none")
p3 <- df %>%
filter(p.adjust <= 0.05, NES < 0) %>%
select(Description, NES, Group) %$%
split(., Group) %>%
lapply(dplyr::slice, seq(5)) %>%
bind_rows() %>%
dplyr::slice(seq(25)) %>%
mutate(., x = 0, y = rev(seq(nrow(.)))) %>%
ggplot(aes(x, y, label = Description, col = Group)) +
geom_text(hjust = 0) +
theme_void() +
xlim(0, 1) +
labs(title = "Selected terms for down-regulated genes") +
scale_color_manual(values = pal.major) +
guides(col = "none")
plot_grid(plotlist = list(p3, p1, p2), ncol = 3, rel_widths = c(0.9, 1, 0.9))
Export source data
p1$data %>%
write.table("source_data/F5l.tsv", sep = "\t", dec = ".", row.names = F)## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 12188442 651 39451381 2107.0 39451381 2107.0
## Vcells 1081334605 8250 2986905661 22788.3 9054649287 69081.5Figure 6
Figures 6c,e,f where made with GraphPad Prism. Data are available in
Phagocytosis_assay_triplicates_raw_data_HH.tsv (Copenhagen
experiment) or HOUSTON.tsv (Houston experiment).
Figure 6b
dat <- read.delim("Phagocytosis_assay_triplicates_raw_data_HH.tsv",
header = T, dec = ",") %>%
tidyr::pivot_longer(names_to = "group", cols = -1, values_to = "value") %>%
mutate(group = gsub(".1|.2", "", group) %>% factor(labels = c("PBS","LPS","MSA CSF","CTRL CSF","PD CSF"))) %>%
mutate(group = factor(group, levels = c("PBS","LPS","CTRL CSF","MSA CSF","PD CSF"))) %>%
group_by(Time, group) %>%
summarize(mean = mean(value, na.rm = T),
sd = sd(value, na.rm = T))## `summarise()` has regrouped the output.
## ℹ Summaries were computed grouped by Time and group.
## ℹ Output is grouped by Time.
## ℹ Use `summarise(.groups = "drop_last")` to silence this message.
## ℹ Use `summarise(.by = c(Time, group))` for per-operation grouping
## (`?dplyr::dplyr_by`) instead.dat %>%
ggplot(aes(Time, mean, group=group, color=group)) +
geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd), width=.1) +
geom_point(size = 0.5) +
theme_bw() +
labs(x = "Time (hours)", y = "pHrodo-labelled E. coli uptake - intensity ratio [AU]", col = "Stimulation") +
scale_color_manual(values = pal.major) +
theme(line = element_blank()) -> p
p
p$data %>%
write.table("source_data/F6b.tsv", sep = "\t", dec = ".", row.names = F)Figure 6d
dat.raw <- read.table("Cytokine_summary.tsv", header = TRUE, sep = "\t", dec = ",")
dat.tmp <- dat.raw %>%
melt(id.vars = c("Sample","Group","Condition")) %>%
mutate(variable = variable %>%
as.character() %>%
gsub(".", "-", ., fixed = T) %>%
as.factor()) %>%
filter(Condition == "Media", !Group %in% c("PBS","LPS")) %>%
filter(Condition == "Media") %>%
mutate(Group = Group %>% factor(levels = c("CTRL","MSA","PD")),
variable = variable %>% factor())
dat.stat <- dat.raw %>%
select(!IL.8) %>%
mutate(Group = factor(Group, levels = c("PBS","LPS","CTRL","MSA","PD"))) %>%
filter(!Group %in% c("PBS","LPS"))
res.il10 <- FSA::dunnTest(IL10 ~ Group, data = dat.stat %>% {`colnames<-`(., gsub(".", "", colnames(.), fixed = T))}, method = "bh")## Registered S3 methods overwritten by 'FSA':
## method from
## confint.boot car
## hist.boot carstat.test <- dat.tmp %>%
filter(variable == "IL-10") %>%
select(Group, value) %>%
as.data.frame() %>%
rstatix::wilcox_test(value ~ Group) %>%
mutate(p = res.il10$res$P.unadj,
p.adj = res.il10$res$P.adj %>% formatC(digits = 2),
p.adj.signif = c("*","ns","ns")) %>%
rstatix::add_xy_position(x = "Group")
dat.tmp %>%
filter(variable == "IL-10") %>%
ggplot(aes(Group, value)) +
geom_point(aes(fill = Group), size = 3, position = position_jitter(width = 0.3), pch = 21, color = "black") +
theme_bw() +
theme(line = element_blank()) +
stat_pvalue_manual(stat.test, label = "p.adj", tip.length = 0.01, backet.nudge.y = 3, hide.ns = F) +
scale_fill_manual(values = pal.major) +
labs(x = "", y = "IL-10 pg/mL") +
guides(fil = "none") -> p
p
Export source data
p$data %>%
write.table("source_data/F6d.tsv", sep = "\t", dec = ".", row.names = F)Figures 6g-i
dat.melt <- read.table("Microglia+CSF_samples_aSyn_sCD163.tsv", header = T, sep = "\t", dec = ",") %>%
melt(id.vars = c("diagnosis", "sex")) %>%
mutate(diagnosis = factor(diagnosis, levels = c("CTRL","MSA","IPD"), labels = c("CTRL","MSA","PD")))Figure 6g
dat.melt %>%
filter(variable == "sCD163_ng.mL_CSF") %>%
ggplot(aes(diagnosis, value)) +
geom_boxplot(aes(fill = diagnosis)) +
theme_bw() +
labs(x = "", y = "sCD163 ng/ml", fill = "Diagnosis") +
scale_fill_manual(values = pal.major) +
stat_compare_means(method = "t.test", comparisons = list(c("CTRL","MSA"), c("MSA","PD"), c("CTRL","PD"))) +
guides(fill = "none") +
theme(line = element_blank()) -> p
p
Export source data
p$data %>%
write.table("source_data/F6g.tsv", sep = "\t", dec = ".", row.names = F)Figure 6h
dat.melt %>%
filter(variable == "aSyn_pg.mL") %>%
ggplot(aes(diagnosis, value)) +
geom_boxplot(aes(fill = diagnosis)) +
theme_bw() +
labs(x = "", y = "aSyn pg/ml", fill = "Diagnosis") +
scale_fill_manual(values = pal.major) +
stat_compare_means(method = "t.test", comparisons = list(c("CTRL","MSA"), c("MSA","PD"), c("CTRL","PD"))) +
guides(fill = "none") +
theme(line = element_blank()) -> p
p## Warning: Removed 36 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 36 rows containing non-finite outside the scale range
## (`stat_signif()`).
p$data %>%
write.table("source_data/F6h.tsv", sep = "\t", dec = ".", row.names = F)Figure 6i
dat.melt %>%
filter(variable %in% c("aSyn_pg.mL","sCD163_ng.mL_CSF")) %>%
select(-sex) %>%
mutate(id = rep(seq(63), 2)) %>%
tidyr::pivot_wider(names_from = variable, values_from = value) %>%
ggplot(aes(aSyn_pg.mL, sCD163_ng.mL_CSF)) +
geom_point(aes(fill = diagnosis), pch = 21, color = "black") +
theme_bw() +
labs(x = "aSyn pg/ml", y = "sCD163, ng/ml", fill = "") +
geom_smooth(method = MASS::rlm, se = FALSE, color = "black") +
stat_poly_eq(aes(label = paste(after_stat(rr.label), after_stat(p.value.label), sep = "*\", \"*")), color = "black") +
scale_fill_manual(values = pal.major) +
theme(line = element_blank()) -> p
p## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 36 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 36 rows containing non-finite outside the scale range
## (`stat_poly_eq()`).## Warning: Removed 36 rows containing missing values or values outside the scale range
## (`geom_point()`).
Export source data
p$data %>%
write.table("source_data/F6i.tsv", sep = "\t", dec = ".", row.names = F)Figure 6j
Here, we use data from Rydbirk et al. on differentially expressed proteins in the soluble fraction of CSF pools.
dat <- read.delim("Soluble_fraction.txt")
msa.dep <- dat %>%
filter(Disease.group == "MSA") %>%
pull(Gene.name)
pd.dep <- dat %>%
filter(Disease.group == "PD") %>%
pull(Gene.name)
overlap <- intersect(msa.dep, pd.dep)
highlight_proteins <- c("CTSS", "FCGR2A", "LAMP1", "CTSD", "CLTC") # From suppl. table 14, KEGG phagosome or lysosome pathways
msa.dep %<>% .[!. %in% overlap]
pd.dep %<>% .[!. %in% overlap]
overlap %<>% .[!. %in% highlight_proteins]
string_db <- STRINGdb$new(
version = "12.0", # or latest available
species = 9606, # 9606 = Homo sapiens
score_threshold = 400, # confidence score cutoff
input_directory = ""
)
genes <- c(msa.dep, pd.dep, overlap, highlight_proteins) %>% unique()
mapped_genes <- string_db$map(
data.frame(gene = genes),
"gene",
removeUnmappedRows = TRUE
)## Warning: we couldn't map to STRING 5% of your identifiers# Get interactions
interactions <- string_db$get_interactions(mapped_genes$STRING_id)
# Convert to igraph object
ppi_graph <- graph_from_data_frame(interactions, directed = FALSE)
# Create label mapping: STRING ID to gene name
string_to_gene <- mapped_genes %>%
select(STRING_id, gene) %>%
distinct()
# Add labels to graph nodes
V(ppi_graph)$label <- string_to_gene$gene[match(V(ppi_graph)$name, string_to_gene$STRING_id)]
custom_colors <- c(
"CTSS" = "firebrick",
"FCGR2A" = "purple2",
"LAMP1" = "purple2",
"CTSD" = "navyblue",
"CLTC" = "navyblue",
setNames(rep("tomato", length(msa.dep)), msa.dep),
setNames(rep("steelblue", length(pd.dep)), pd.dep),
setNames(rep("orchid", length(overlap)), overlap))
# Apply color mapping to node attributes
V(ppi_graph)$node_color <- custom_colors[V(ppi_graph)$label]
# Get node labels for each edge endpoint
edge_ends <- ends(ppi_graph, es = E(ppi_graph), names = FALSE)
source_labels <- V(ppi_graph)$label[edge_ends[, 1]]
target_labels <- V(ppi_graph)$label[edge_ends[, 2]]
# Assign edge width
E(ppi_graph)$edge_width <- ifelse(
source_labels %in% highlight_proteins & target_labels %in% highlight_proteins,
0.5, 0.1
)
# Compute node sizes based on label
V(ppi_graph)$node_size <- sapply(V(ppi_graph)$label, \(x) if (x %in% overlap) 3 else if (x %in% highlight_proteins) 5 else 2)
V(ppi_graph)$label_type <- ifelse(
V(ppi_graph)$label %in% highlight_proteins,
"highlight", "other"
)
# Plot the network with size and color
ggraph(ppi_graph, layout = "fr") +
geom_edge_link(aes(width = I(edge_width)), alpha = 0.4) +
geom_node_point(aes(color = I(node_color), size = I(node_size)), show.legend = FALSE) +
geom_node_label(data = function(x) { x[x$label_type == "highlight", ] },
aes(label = label), size = 3, label.padding = unit(0.15, "lines"), repel = TRUE, show.legend = F) +
geom_node_text(data = function(x) { x[x$label_type == "other", ] },
aes(label = label), size = 2, repel = TRUE, show.legend = F) +
theme_void()
Export source data
ppi_graph %>%
vertex.attributes() %>%
as.data.frame() %>%
write.table("source_data/F6j_nodes.tsv", sep = "\t", dec = ".", row.names = F)
ppi_graph %>%
as_edgelist() %>%
as.data.frame() %>%
setNames(c("from", "to")) %>%
cbind(edge.attributes(ppi_graph)) %>%
write.table("source_data/F6j_edges.tsv", sep = "\t", dec = ".", row.names = F)Create source data file
Not rerun here
tsv_files <- dir("source_data", pattern = ".tsv", full.names = T)
nn <- tsv_files %>%
basename() %>%
tools::file_path_sans_ext() %>%
gsub("F", "Figure ", ., fixed = T)
tsv_files %>%
lapply(., read.delim, check.names = FALSE, sep = "\t", dec = ".") %>%
setNames(nn) %>%
writexl::write_xlsx("source_data/source_data.xlsx")Extended Data Figure 1
Load data
samples <- anno.major %>%
names() %>%
strsplit("!!") %>%
sget(1) %>%
unique()
crm <- qread("crm.qs",
nthreads = 10)
crm$theme <- theme_bw()1a-f
mtp <- crm$selectMetrics(ids = c(1:4,6,19))
plot.list <- mtp %>%
lapply(\(met) crm$plotSummaryMetrics(metrics = met, comp.group = "sample", second.comp.group = "group") +
scale_fill_manual(values = pal.major) +
labs(x = "") +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank())) ## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.plot.list %>%
plot_grid(plotlist = ., labels = letters[1:6], ncol = 2)
1g
crm$plotSummaryMetrics(metrics = crm$selectMetrics(ids = 20), comp.group = "sample", second.comp.group = "group") +
scale_fill_manual(values = pal.major) +
labs(x = "") +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
legend.position = "right") -> p## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.p
1h
crm$plotFilteredCells(doublet.method = "doubletdetection", depth.cutoff = 5e2, size = 0.2, alpha = 0.1) +
dotSize(3) -> p## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.p
1i
crm$plotFilteredCells(type = "bar", doublet.method = "doubletdetection", depth.cutoff = 5e2)$data %>%
filter(sample != "MSA_1406") %>%
mutate(sample = strsplit(sample, "_") %>%
sget(1)) %$%
split(., sample) %>%
lapply(\(x) split(x, x$filter)) %>%
lapply(lapply, \(df) mutate(df, sample = paste0(sample, seq(nrow(df))))) %>%
lapply(bind_rows) %>%
bind_rows() %>%
mutate(sample = factor(sample, levels = c(paste0("CTRL", seq(10)), paste0("MSA", seq(7)), paste0("PD", seq(12))))) %>%
ggplot(aes(sample, pct, fill = filter)) +
geom_bar(stat = "identity") +
geom_text_repel(aes(label = sprintf("%0.2f", round(pct, digits = 2))),
position = position_stack(vjust = 0.5),
direction = "y",
size = 2.5) +
crm$theme +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(x = "", y = "Percentage cells filtered") +
theme(legend.position = "bottom") -> p
p
Extended Data Figure 2
These figures cannot be reproduced here due to GDPR. Leaving the code for visibility.
Load data
crm <- qread("crm.qs", nthreads = 10)2a
crm$plotCbCells() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))2b
crm$plotCbAmbGenes() +
theme(line = element_blank(),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
axis.ticks.x = element_line()) -> p
p## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 19187092 1024.8 39451381 2107.0 39451381 2107.0
## Vcells 3010817893 22970.8 4301320151 32816.5 9054649287 69081.52c
c("RBFOX3","MOG","VCAN","AQP4","CSF1R","PDGFRB","PTPRC","MRC1","GAD1","SLC17A7","PPP1R1B") %>%
sort() %>%
lapply(function(g) con$plotGraph(embedding = "UMAP", gene=g, title=g, size=0.1, plot.na = F, palette = colorRampPalette(c("grey90","firebrick"))) + theme(line = element_blank())) -> plot.list
plot.list %>%
cowplot::plot_grid(plotlist=., ncol=2)
Extended Data Figure 3
con$embedding <- con$embeddings$UMAP
sample.groups <- con$samples %>%
names() %>%
setNames(grepl.replace(., c("CTRL","MSA","PD")), .)
cao <- Cacoa$new(data.object = con,
sample.groups = ifelse(grepl("CTRL", sample.groups), "CTRL", "DISEASE") %>%
setNames(names(sample.groups)),
cell.groups = anno.major,
ref.level = "CTRL",
target.level = "DISEASE",
n.cores = 32)
meta <- read.delim("metadata.tsv", sep = "\t") %>%
filter(sample %in% names(con$samples)) %>%
tibble::column_to_rownames("sample") %>%
dplyr::select(-condition)
meta %<>%
mutate(sample = rownames(.))
spc <- con$getDatasetPerCell()
mpc <- spc %>%
data.frame(sample = ., cid = names(.))
for (cc in colnames(meta)) {
mpc[[cc]] <- meta[[cc]][match(mpc$sample, meta$sample)]
}
mpc$subtype[mpc$subtype == ""] <- NAp <- con$plotGraph(colors = setNames(mpc$age, mpc$cid), plot.na = F, embedding = "UMAP_1_0.001_5", mark.groups = F, show.legend = T, title = "Age", size = 0.3, alpha = 0.3) +
scale_color_gradient(low = "grey", high = "firebrick") +
theme(line = element_blank()) +
labs(col = "Years")## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.p
p <- con$plotGraph(colors = setNames(mpc$pmi, mpc$cid), plot.na = F, embedding = "UMAP_1_0.001_5", mark.groups = F, show.legend = T, title = "PMI", size = 0.3, alpha = 0.3) +
scale_color_gradient(low = "grey", high = "firebrick") +
theme(line = element_blank()) +
labs(col = "Hours")## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.p
p <- con$plotGraph(colors = setNames(mpc$disease_duration, mpc$cid), plot.na = F, embedding = "UMAP_1_0.001_5", mark.groups = F, show.legend = T, title = "Disease duration", size = 0.3, alpha = 0.3) +
scale_color_gradient(low = "grey", high = "firebrick") +
theme(line = element_blank()) +
labs(col = "Years")## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.p
p <- con$plotGraph(groups = setNames(mpc$sample, mpc$cid), plot.na = F, embedding = "UMAP_1_0.001_5", mark.groups = F, show.legend = T, shuffle.colors = T, title = "Sample", size = 0.3, alpha = 0.3) +
theme(line = element_blank()) + dotSize(3)
p
p <- con$plotGraph(groups = setNames(mpc$brain_bank, mpc$cid), plot.na = F, embedding = "UMAP_1_0.001_5", mark.groups = F, show.legend = T, shuffle.colors = T, title = "Brain bank", size = 0.3, alpha = 0.3) +
theme(line = element_blank()) + dotSize(3)
p
p <- con$plotGraph(groups = setNames(mpc$sex, mpc$cid), plot.na = F, embedding = "UMAP_1_0.001_5", mark.groups = F, show.legend = T, shuffle.colors = T, title = "Sex", size = 0.3, alpha = 0.3) +
theme(line = element_blank()) + dotSize(3)
p
p <- con$plotGraph(groups = setNames(mpc$subtype, mpc$cid), plot.na = F, embedding = "UMAP_1_0.001_5", mark.groups = F, show.legend = T, shuffle.colors = T, title = "Subtype", size = 0.3, alpha = 0.3) +
theme(line = element_blank()) + dotSize(3)
p
p <- con$plotGraph(groups = setNames(mpc$extract, mpc$cid), plot.na = F, embedding = "UMAP_1_0.001_5", mark.groups = F, show.legend = T, shuffle.colors = T, title = "Extract", size = 0.3, alpha = 0.3) +
theme(line = element_blank()) + dotSize(3)
p
p <- con$plotGraph(groups = setNames(mpc$flowcell, mpc$cid), plot.na = F, embedding = "UMAP_1_0.001_5", mark.groups = F, show.legend = T, shuffle.colors = T, title = "Flowcell", size = 0.3, alpha = 0.3) +
theme(line = element_blank()) + dotSize(3)
p
Extended Data Figure 4 & EDT3
Load metadata
meta <- read.delim("metadata.tsv", sep = "\t") %>%
filter(sample %in% names(con$samples)) %>%
tibble::column_to_rownames("sample") %>%
mutate(sample = rownames(.)) %>%
dplyr::select(-condition)4a
con_msa <- con$clone()
con_msa$embedding <- con$embeddings$UMAP
con_msa$samples <- con$samples %>% .[!grepl("PD", names(.))]
sample.groups <- con_msa$samples %>%
names() %>%
setNames(grepl.replace(., c("CTRL","MSA")), .)
cao_msa <- Cacoa$new(data.object = con_msa,
sample.groups = ifelse(grepl("CTRL", sample.groups), "CTRL", "MSA") %>%
setNames(names(sample.groups)),
cell.groups = anno.major,
ref.level = "CTRL",
target.level = "MSA",
n.cores = 32)
cao_msa$estimateExpressionShiftMagnitudes()## Filtering data...## Excluding cell types Exc. neurons that don't have enough samples## done!## Calculating pairwise distances using dist='cor'...## Done!cao_msa$estimateMetadataSeparation(sample.meta = meta %>% filter(!grepl("PD", sample)) %>% dplyr::select(-brain_bank, -sample),
space = "expression.shifts")plot.list <- list(
cao_msa$plotSampleDistances(space = "expression.shifts", show.sample.size = T, method = "UMAP", title = "Condition"),
cao_msa$plotSampleDistances(space = "expression.shifts", method = "UMAP", show.sample.size = T, sample.colors = meta$age %>% setNames(rownames(meta)), title = "Age"),
cao_msa$plotSampleDistances(space = "expression.shifts", method = "UMAP", sample.colors = meta$disease_duration %>% setNames(rownames(meta)), show.sample.size = T, title = "Disease duration"),
cao_msa$plotSampleDistances(space = "expression.shifts", method = "UMAP", sample.colors = meta$extract %>% setNames(rownames(meta)), show.sample.size = T, title = "Extract batch"),
cao_msa$plotSampleDistances(space = "expression.shifts", method = "UMAP", sample.colors = meta$flowcell %>% setNames(rownames(meta)), show.sample.size = T, title = "Flowcell"),
cao_msa$plotSampleDistances(space = "expression.shifts", method = "UMAP", show.sample.size = T, sample.colors = meta$pmi %>% setNames(rownames(meta)), title = "PMI"),
cao_msa$plotSampleDistances(space = "expression.shifts", method = "UMAP", sample.colors = meta$sex %>% setNames(rownames(meta)), show.sample.size = T, title = "Sex"),
cao_msa$plotSampleDistances(space = "expression.shifts", method = "UMAP", sample.colors = meta$subtype %>% setNames(rownames(meta)), show.sample.size = T, title = "Subtype"),
cao_msa$test.results$metadata.separation$padjust %>%
data.frame(p = .) %>%
mutate(pp = -log10(p), padj = formatC(p, digits = 3)) %>%
mutate(padj = sapply(padj, \(x) if (x > 0.05) "" else paste("p.adj = ", x, sep = ""))) %>%
tibble::rownames_to_column("covariate") %>%
ggplot(aes(covariate, pp)) +
geom_bar(stat = "identity", fill = "lightblue4") +
ylim(0, pmax(2, max(-log10(cao_msa$test.results$metadata.separation$padjust)))) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
labs(x = "", y = "-log10(adj. p)", title = "Association significance") +
geom_hline(yintercept = -log10(0.05), colour = "red3") +
geom_text(aes(label = padj, y = pp + 0.1), size = 8)
)## Warning in uwot(X = X, n_neighbors = n_neighbors, n_components = n_components,
## : n_components > number of columns in input data: 2 > 1, this may give poor or
## unexpected results
## Warning in uwot(X = X, n_neighbors = n_neighbors, n_components = n_components,
## : n_components > number of columns in input data: 2 > 1, this may give poor or
## unexpected results
## Warning in uwot(X = X, n_neighbors = n_neighbors, n_components = n_components,
## : n_components > number of columns in input data: 2 > 1, this may give poor or
## unexpected results
## Warning in uwot(X = X, n_neighbors = n_neighbors, n_components = n_components,
## : n_components > number of columns in input data: 2 > 1, this may give poor or
## unexpected results
## Warning in uwot(X = X, n_neighbors = n_neighbors, n_components = n_components,
## : n_components > number of columns in input data: 2 > 1, this may give poor or
## unexpected results
## Warning in uwot(X = X, n_neighbors = n_neighbors, n_components = n_components,
## : n_components > number of columns in input data: 2 > 1, this may give poor or
## unexpected results
## Warning in uwot(X = X, n_neighbors = n_neighbors, n_components = n_components,
## : n_components > number of columns in input data: 2 > 1, this may give poor or
## unexpected results
## Warning in uwot(X = X, n_neighbors = n_neighbors, n_components = n_components,
## : n_components > number of columns in input data: 2 > 1, this may give poor or
## unexpected resultscowplot::plot_grid(plotlist = plot.list, ncol = 5) -> p
p
4b
con_pd <- con$clone()
con_pd$embedding <- con$embeddings$UMAP
con_pd$samples <- con$samples %>% .[!grepl("MSA", names(.))]
sample.groups <- con_pd$samples %>%
names() %>%
setNames(grepl.replace(., c("CTRL","PD")), .)
cao_pd <- Cacoa$new(data.object = con_pd,
sample.groups = ifelse(grepl("CTRL", sample.groups), "CTRL", "PD") %>%
setNames(names(sample.groups)),
cell.groups = anno.major,
ref.level = "CTRL",
target.level = "PD",
n.cores = 32)
cao_pd$estimateExpressionShiftMagnitudes()## Filtering data...## Excluding cell types Exc. neurons that don't have enough samples## done!## Calculating pairwise distances using dist='cor'...## Done!cao_pd$estimateMetadataSeparation(sample.meta = meta %>% filter(!grepl("MSA", sample)) %>% dplyr::select(-disease_duration, -subtype, -sample),
space = "expression.shifts")plot.list <- list(
cao_pd$plotSampleDistances(space = "expression.shifts", show.sample.size = T, method = "UMAP", title = "Condition"),
cao_pd$plotSampleDistances(space = "expression.shifts", method = "UMAP", show.sample.size = T, sample.colors = meta$age %>% setNames(rownames(meta)), title = "Age"),
cao_pd$plotSampleDistances(space = "expression.shifts", method = "UMAP", sample.colors = meta$brain_bank %>% setNames(rownames(meta)), show.sample.size = T, title = "Brain bank"),
cao_pd$plotSampleDistances(space = "expression.shifts", method = "UMAP", sample.colors = meta$extract %>% setNames(rownames(meta)), show.sample.size = T, title = "Extract batch"),
cao_pd$plotSampleDistances(space = "expression.shifts", method = "UMAP", sample.colors = meta$flowcell %>% setNames(rownames(meta)), show.sample.size = T, title = "Flowcell"),
cao_pd$plotSampleDistances(space = "expression.shifts", method = "UMAP", show.sample.size = T, sample.colors = meta$pmi %>% setNames(rownames(meta)), title = "PMI"),
cao_pd$plotSampleDistances(space = "expression.shifts", method = "UMAP", sample.colors = meta$sex %>% setNames(rownames(meta)), show.sample.size = T, title = "Sex"),
cao_pd$test.results$metadata.separation$padjust %>%
data.frame(p = .) %>%
mutate(pp = -log10(p), padj = formatC(p, digits = 3)) %>%
mutate(padj = sapply(padj, \(x) if (x > 0.05) "" else paste("p.adj = ", x, sep = ""))) %>%
tibble::rownames_to_column("covariate") %>%
ggplot(aes(covariate, pp)) +
geom_bar(stat = "identity", fill = "lightblue4") +
ylim(0, pmax(2, max(-log10(cao_pd$test.results$metadata.separation$padjust)))) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
labs(x = "", y = "-log10(adj. p)", title = "Association significance") +
geom_hline(yintercept = -log10(0.05), colour = "red3") +
geom_text(aes(label = padj, y = pp + 0.1), size = 3)
)## Warning in uwot(X = X, n_neighbors = n_neighbors, n_components = n_components,
## : n_components > number of columns in input data: 2 > 1, this may give poor or
## unexpected results
## Warning in uwot(X = X, n_neighbors = n_neighbors, n_components = n_components,
## : n_components > number of columns in input data: 2 > 1, this may give poor or
## unexpected results
## Warning in uwot(X = X, n_neighbors = n_neighbors, n_components = n_components,
## : n_components > number of columns in input data: 2 > 1, this may give poor or
## unexpected results
## Warning in uwot(X = X, n_neighbors = n_neighbors, n_components = n_components,
## : n_components > number of columns in input data: 2 > 1, this may give poor or
## unexpected results
## Warning in uwot(X = X, n_neighbors = n_neighbors, n_components = n_components,
## : n_components > number of columns in input data: 2 > 1, this may give poor or
## unexpected results
## Warning in uwot(X = X, n_neighbors = n_neighbors, n_components = n_components,
## : n_components > number of columns in input data: 2 > 1, this may give poor or
## unexpected results
## Warning in uwot(X = X, n_neighbors = n_neighbors, n_components = n_components,
## : n_components > number of columns in input data: 2 > 1, this may give poor or
## unexpected resultscowplot::plot_grid(plotlist = plot.list, ncol = 4) -> p
p
4c
con_dis <- con$clone()
con_dis$embedding <- con$embeddings$UMAP
con_dis$samples <- con$samples %>% .[!grepl("CTRL", names(.))]
sample.groups <- con_dis$samples %>%
names() %>%
setNames(grepl.replace(., c("PD","MSA")), .)
cao_dis <- Cacoa$new(data.object = con_dis,
sample.groups = ifelse(grepl("PD", sample.groups), "PD", "MSA") %>%
setNames(names(sample.groups)),
cell.groups = anno.major,
ref.level = "PD",
target.level = "MSA",
n.cores = 32)
cao_dis$estimateExpressionShiftMagnitudes()## Filtering data...## Excluding cell types Exc. neurons that don't have enough samples## done!## Calculating pairwise distances using dist='cor'...## Done!cao_dis$estimateMetadataSeparation(sample.meta = meta %>% filter(!grepl("CTRL", sample)) %>% dplyr::select(-sample),
space = "expression.shifts")plot.list <- list(
cao_dis$plotSampleDistances(space = "expression.shifts", show.sample.size = T, method = "UMAP", title = "Condition"),
cao_dis$plotSampleDistances(space = "expression.shifts", method = "UMAP", show.sample.size = T, sample.colors = meta$age %>% setNames(rownames(meta)), title = "Age"),
cao_dis$plotSampleDistances(space = "expression.shifts", method = "UMAP", sample.colors = meta$brain_bank %>% setNames(rownames(meta)), show.sample.size = T, title = "Brain bank"),
cao_dis$plotSampleDistances(space = "expression.shifts", method = "UMAP", sample.colors = meta$disease_duration %>% setNames(rownames(meta)), show.sample.size = T, title = "Disease duration"),
cao_dis$plotSampleDistances(space = "expression.shifts", method = "UMAP", sample.colors = meta$extract %>% setNames(rownames(meta)), show.sample.size = T, title = "Extract batch"),
cao_dis$plotSampleDistances(space = "expression.shifts", method = "UMAP", sample.colors = meta$flowcell %>% setNames(rownames(meta)), show.sample.size = T, title = "Flowcell"),
cao_dis$plotSampleDistances(space = "expression.shifts", method = "UMAP", show.sample.size = T, sample.colors = meta$pmi %>% setNames(rownames(meta)), title = "PMI"),
cao_dis$plotSampleDistances(space = "expression.shifts", method = "UMAP", sample.colors = meta$sex %>% setNames(rownames(meta)), show.sample.size = T, title = "Sex"),
cao_dis$plotSampleDistances(space = "expression.shifts", method = "UMAP", sample.colors = meta$subtype %>% setNames(rownames(meta)), show.sample.size = T, title = "Subtype"),
cao_dis$test.results$metadata.separation$padjust %>%
data.frame(p = .) %>%
mutate(pp = -log10(p), padj = formatC(p, digits = 3)) %>%
mutate(padj = sapply(padj, \(x) if (x > 0.05) "" else paste("p.adj = ", x, sep = ""))) %>%
tibble::rownames_to_column("covariate") %>%
ggplot(aes(covariate, pp)) +
geom_bar(stat = "identity", fill = "lightblue4") +
ylim(0, pmax(2, max(-log10(cao_pd$test.results$metadata.separation$padjust)))) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
labs(x = "", y = "-log10(adj. p)", title = "Association significance") +
geom_hline(yintercept = -log10(0.05), colour = "red3") +
geom_text(aes(label = padj, y = pp + 0.1), size = 8)
)## Warning in uwot(X = X, n_neighbors = n_neighbors, n_components = n_components,
## : n_components > number of columns in input data: 2 > 1, this may give poor or
## unexpected results
## Warning in uwot(X = X, n_neighbors = n_neighbors, n_components = n_components,
## : n_components > number of columns in input data: 2 > 1, this may give poor or
## unexpected results
## Warning in uwot(X = X, n_neighbors = n_neighbors, n_components = n_components,
## : n_components > number of columns in input data: 2 > 1, this may give poor or
## unexpected results
## Warning in uwot(X = X, n_neighbors = n_neighbors, n_components = n_components,
## : n_components > number of columns in input data: 2 > 1, this may give poor or
## unexpected results
## Warning in uwot(X = X, n_neighbors = n_neighbors, n_components = n_components,
## : n_components > number of columns in input data: 2 > 1, this may give poor or
## unexpected results
## Warning in uwot(X = X, n_neighbors = n_neighbors, n_components = n_components,
## : n_components > number of columns in input data: 2 > 1, this may give poor or
## unexpected results
## Warning in uwot(X = X, n_neighbors = n_neighbors, n_components = n_components,
## : n_components > number of columns in input data: 2 > 1, this may give poor or
## unexpected results
## Warning in uwot(X = X, n_neighbors = n_neighbors, n_components = n_components,
## : n_components > number of columns in input data: 2 > 1, this may give poor or
## unexpected results
## Warning in uwot(X = X, n_neighbors = n_neighbors, n_components = n_components,
## : n_components > number of columns in input data: 2 > 1, this may give poor or
## unexpected resultscowplot::plot_grid(plotlist = plot.list, ncol = 5) -> p
p
Extended Data Table 3
Not rerun
list(cao_msa, cao_pd, cao_dis) %>%
lapply(getElement, "test.results") %>%
lget("metadata.separation") %>%
lapply(\(x) x[-1]) %>%
Map(\(dat, nn) bind_cols(dat) %>% data.frame() %>% `rownames<-`(nn), dat = ., nn = lapply(., lapply, names) %>% sget(1)) %>%
setNames(c("MSA", "PD", "dis")) %>%
lapply(tibble::rownames_to_column, var = "covariate") %>%
bind_rows(.id = "comparison") %>%
mutate(comparison = scHelper::grepl.replace(comparison, c("MSA", "PD", "dis"), c("CTRL vs MSA", "CTRL vs PD", "PD vs MSA"))) %>%
write.table("Table SX - Cacoa covariate analysis.tsv", sep = "\t", dec = ".", row.names = F)Extended Data Figure 5
To investigate effects from co-variates, we ran scITD on all cell types for each comparison. We don’t determine factors here as it takes a significant amount of time, but we keep the code in the first instance.
We tested different levels of var_scale_power, here we’re just showing var_scale_power = 0.5 as we didn’t find any effects except for OPCs. Per default, we set vargenes_method=“norm_var_pvals” and adjusted vargenes_thresh to obtain around 1,000 var. genes, but in some instances we took the top most variable genes instead.
Neurons, preparation
con.tmp <- qread("cao_neurons.qs", nthreads = 10)$data.object
anno <- qread("anno_neurons.qs")
anno %<>%
collapseAnnotation("GABA") %>%
collapseAnnotation("GLU") %>%
renameAnnotation("GABA", "Inh. neurons") %>%
renameAnnotation("GLU", "Exc. neurons") %>%
collapseAnnotation("MSN")## Collapsing 11 labels containing 'GABA' in their name into one label.
## Collapsing 2 labels containing 'GLU' in their name into one label.
## Collapsing 3 labels containing 'MSN' in their name into one label.cm.raw.all <- con.tmp$getJointCountMatrix(raw = T) %>%
Matrix::t() %>%
.[!grepl("MT-|RPS|RPL", rownames(.)), ]
meta.all <- con.tmp$getDatasetPerCell() %>%
data.frame(donors = .) %>%
mutate(ctypes = anno[rownames(.)] %>% unname()) %>%
.[complete.cases(.), ]
metadata.all <- read.delim("metadata.tsv")
for (cc in colnames(metadata.all)[-1]) {
meta.all[[cc]] <- metadata.all[[cc]][match(meta.all$donors, metadata.all$sample)]
}
# Convert integers to numeric
meta.all$age %<>% as.numeric()
meta.all$disease_duration %<>% as.numeric()CTRL vs MSA
meta <- meta.all %>%
filter(condition != "PD") %>%
mutate(donors = factor(donors),
ctypes = factor(ctypes))
metadata <- metadata.all %>%
filter(condition != "PD")
cm.raw <- cm.raw.all %>%
.[, colnames(.) %in% rownames(meta)]
# set up project parameters
param_list <- initialize_params(ctypes_use = meta$ctypes %>% levels(),
ncores = 31,
rand_seed = 10)
# create project container
itd_container <- make_new_container(count_data=cm.raw %>% .[, colnames(.) %in% rownames(meta)],
meta_data=meta,
params=param_list,
label_donor_sex = F)## Warning in make_new_container(count_data = cm.raw %>% .[, colnames(.) %in% :
## Cell type names cannot have special characters '.', '_', or ':'. Removing these
## characters and concatenating name components.itd_container %<>% form_tensor(donor_min_cells=1,
norm_method='trim',
scale_factor=10000,
vargenes_method='norm_var_pvals',
vargenes_thresh=0.7,
scale_var = TRUE,
var_scale_power = 0.5)## parsing data matrix by cell/tissue type...## cleaning data...## Keeping 10 donors. All donors have at least 1 cells in each cell type included.## collapsing count matrices from cells to donors (aka pseudobulk operation)...## normalizing data...## calculating gene overdispersion factors...## selecting highly variable genes from each cell type...## scaling variance...## forming tensor...## Complete!print(length(itd_container[["all_vargenes"]]))## [1] 1179# get assistance with rank determination
itd_container %<>% determine_ranks_tucker(max_ranks_test=c(10,15),
shuffle_level='cells',
num_iter=10,
norm_method='trim',
scale_factor=10000,
scale_var=TRUE,
var_scale_power=0.5)
itd_container$plots$rank_determination_plotitd_container %<>% run_tucker_ica(ranks=c(5,10),
tucker_type = 'regular',
rotation_type = 'hybrid')
# get donor scores-metadata associations
itd_container %<>% get_meta_associations(vars_test=c('sex','pmi', "age", "flowcell", "extract"), # Omitting brain bank
stat_use='pval')
# plot donor scores
itd_container %<>% plot_donor_matrix(meta_vars=c('sex','pmi', "age", "flowcell", "extract"), # Omitting brain bank
show_donor_ids = TRUE,
add_meta_associations="pval")
# show the donor scores heatmap
p <- itd_container$plots$donor_matrix
p
CTRL vs PD
meta <- meta.all %>%
filter(condition != "MSA") %>%
mutate(donors = factor(donors),
ctypes = factor(ctypes))
metadata <- metadata.all %>%
filter(condition != "MSA")
cm.raw <- cm.raw.all %>%
.[, colnames(.) %in% rownames(meta)]
# set up project parameters
param_list <- initialize_params(ctypes_use = meta$ctypes %>% levels(),
ncores = 31,
rand_seed = 10)
# create project container
itd_container <- make_new_container(count_data=cm.raw,
meta_data=meta,
params=param_list,
label_donor_sex = F)## Warning in make_new_container(count_data = cm.raw, meta_data = meta, params =
## param_list, : Cell type names cannot have special characters '.', '_', or ':'.
## Removing these characters and concatenating name components.itd_container %<>% form_tensor(donor_min_cells=1,
norm_method='trim',
scale_factor=10000,
vargenes_method='norm_var',
vargenes_thresh=500,
scale_var = TRUE,
var_scale_power = 0.5)## parsing data matrix by cell/tissue type...## cleaning data...## Keeping 9 donors. All donors have at least 1 cells in each cell type included.## collapsing count matrices from cells to donors (aka pseudobulk operation)...## normalizing data...## calculating gene overdispersion factors...## selecting highly variable genes from each cell type...## scaling variance...## forming tensor...## Complete!print(length(itd_container[["all_vargenes"]]))## [1] 1084itd_container %<>% run_tucker_ica(ranks=c(5,10),
tucker_type = 'regular',
rotation_type = 'hybrid')
# get donor scores-metadata associations
itd_container %<>% get_meta_associations(vars_test=c('sex','pmi', "age", "brain_bank", "flowcell", "extract"),
stat_use='pval')
# plot donor scores
itd_container %<>% plot_donor_matrix(meta_vars=c('sex','pmi', "age", "brain_bank", "flowcell", "extract"),
show_donor_ids = TRUE,
add_meta_associations='pval')
# show the donor scores heatmap
p <- itd_container$plots$donor_matrix
p
PD vs MSA
meta <- meta.all %>%
filter(condition != "CTRL") %>%
mutate(donors = factor(donors),
ctypes = factor(ctypes))
metadata <- metadata.all %>%
filter(condition != "CTRL")
cm.raw <- cm.raw.all %>%
.[, colnames(.) %in% rownames(meta)]
# set up project parameters
param_list <- initialize_params(ctypes_use = meta$ctypes %>% levels(),
ncores = 31,
rand_seed = 10)
# create project container
itd_container <- make_new_container(count_data=cm.raw,
meta_data=meta,
params=param_list,
label_donor_sex = F)## Warning in make_new_container(count_data = cm.raw, meta_data = meta, params =
## param_list, : Cell type names cannot have special characters '.', '_', or ':'.
## Removing these characters and concatenating name components.itd_container %<>% form_tensor(donor_min_cells=1,
norm_method='trim',
scale_factor=10000,
vargenes_method='norm_var',
vargenes_thresh=500,
scale_var = TRUE,
var_scale_power = 0.5)## parsing data matrix by cell/tissue type...## cleaning data...## Keeping 7 donors. All donors have at least 1 cells in each cell type included.## Consider using fewer cell types or reducing the donor_min_cells parameter to include more donors.## collapsing count matrices from cells to donors (aka pseudobulk operation)...## normalizing data...## calculating gene overdispersion factors...## selecting highly variable genes from each cell type...## scaling variance...## forming tensor...## Complete!print(length(itd_container[["all_vargenes"]]))## [1] 1084itd_container %<>% run_tucker_ica(ranks=c(5,10),
tucker_type = 'regular',
rotation_type = 'hybrid')
# get donor scores-metadata associations
itd_container %<>% get_meta_associations(vars_test=c('sex','pmi', "age", "brain_bank", "flowcell", "extract"),
stat_use='pval')
# plot donor scores
itd_container %<>% plot_donor_matrix(meta_vars=c('sex','pmi', "age", "brain_bank", "flowcell", "extract"),
show_donor_ids = TRUE,
add_meta_associations='pval')
# show the donor scores heatmap
p <- itd_container$plots$donor_matrix
p
Astrocytes, preparation
con.tmp <- qread("con_astrocytes.qs", nthreads = 10)
anno <- qread("anno_astro.qs")
cm.raw.all <- con.tmp$getJointCountMatrix(raw = T) %>%
.[, !grepl("MT-|RPS|RPL", colnames(.))] %>%
Matrix::t()
meta.all <- con.tmp$getDatasetPerCell() %>%
.[names(.) %in% names(anno)] %>%
data.frame(donors = .) %>%
mutate(ctypes = anno[rownames(.)] %>% unname()) %>%
.[complete.cases(.), ]
metadata.all <- read.delim("metadata.tsv")
for (cc in colnames(metadata.all)[-1]) {
meta.all[[cc]] <- metadata.all[[cc]][match(meta.all$donors, metadata.all$sample)]
}
# Convert integers to numeric
meta.all$age %<>% as.numeric()
meta.all$disease_duration %<>% as.numeric()CTRL vs MSA
meta <- meta.all %>%
filter(condition != "PD") %>%
mutate(donors = factor(donors),
ctypes = factor(ctypes))
metadata <- metadata.all %>%
filter(condition != "PD")
cm.raw <- cm.raw.all %>%
.[, colnames(.) %in% rownames(meta)]
# set up project parameters
param_list <- initialize_params(ctypes_use = meta$ctypes %>% levels(),
ncores = 31,
rand_seed = 10)
# create project container
itd_container <- make_new_container(count_data=cm.raw %>% .[, colnames(.) %in% rownames(meta)],
meta_data=meta,
params=param_list,
label_donor_sex = F)## Warning in make_new_container(count_data = cm.raw %>% .[, colnames(.) %in% :
## Cell type names cannot have special characters '.', '_', or ':'. Removing these
## characters and concatenating name components.itd_container %<>% form_tensor(donor_min_cells=5,
norm_method='trim',
scale_factor=10000,
vargenes_method='norm_var_pvals',
vargenes_thresh=0.05,
scale_var = TRUE,
var_scale_power = 0.5)## parsing data matrix by cell/tissue type...## cleaning data...## Keeping 17 donors. All donors have at least 5 cells in each cell type included.## collapsing count matrices from cells to donors (aka pseudobulk operation)...## normalizing data...## calculating gene overdispersion factors...## selecting highly variable genes from each cell type...## scaling variance...## forming tensor...## Complete!print(length(itd_container[["all_vargenes"]]))## [1] 704itd_container %<>% run_tucker_ica(ranks=c(5,8),
tucker_type = 'regular',
rotation_type = 'hybrid')
# get donor scores-metadata associations
itd_container %<>% get_meta_associations(vars_test=c('sex','pmi', "age", "flowcell", "extract"), # Omitting brain bank
stat_use='pval')
# plot donor scores
itd_container %<>% plot_donor_matrix(meta_vars=c('sex','pmi', "age", "flowcell", "extract"), # Omitting brain bank
show_donor_ids = TRUE,
add_meta_associations='pval')
# show the donor scores heatmap
p <- itd_container$plots$donor_matrix
p
CTRL vs PD
meta <- meta.all %>%
filter(condition != "MSA") %>%
mutate(donors = factor(donors),
ctypes = factor(ctypes))
metadata <- metadata.all %>%
filter(condition != "MSA")
cm.raw <- cm.raw.all %>%
.[, colnames(.) %in% rownames(meta)]
# set up project parameters
param_list <- initialize_params(ctypes_use = meta$ctypes %>% levels(),
ncores = 31,
rand_seed = 10)
# create project container
itd_container <- make_new_container(count_data=cm.raw,
meta_data=meta,
params=param_list,
label_donor_sex = F)## Warning in make_new_container(count_data = cm.raw, meta_data = meta, params =
## param_list, : Cell type names cannot have special characters '.', '_', or ':'.
## Removing these characters and concatenating name components.itd_container %<>% form_tensor(donor_min_cells=5,
norm_method='trim',
scale_factor=10000,
vargenes_method='norm_var_pvals',
vargenes_thresh=.05,
scale_var = TRUE,
var_scale_power = 0.5)## parsing data matrix by cell/tissue type...## cleaning data...## Keeping 22 donors. All donors have at least 5 cells in each cell type included.## collapsing count matrices from cells to donors (aka pseudobulk operation)...## normalizing data...## calculating gene overdispersion factors...## selecting highly variable genes from each cell type...## scaling variance...## forming tensor...## Complete!print(length(itd_container[["all_vargenes"]]))## [1] 871itd_container %<>% run_tucker_ica(ranks=c(5,9),
tucker_type = 'regular',
rotation_type = 'hybrid')
# get donor scores-metadata associations
itd_container %<>% get_meta_associations(vars_test=c('sex','pmi', "age", "brain_bank", "flowcell", "extract"), # Omitting brain bank
stat_use='pval')
# plot donor scores
itd_container %<>% plot_donor_matrix(meta_vars=c('sex','pmi', "age", "brain_bank", "flowcell", "extract"), # Omitting brain bank
show_donor_ids = TRUE,
add_meta_associations='pval')
# show the donor scores heatmap
p <- itd_container$plots$donor_matrix
p
PD vs MSA
meta <- meta.all %>%
filter(condition != "CTRL") %>%
mutate(donors = factor(donors),
ctypes = factor(ctypes))
metadata <- metadata.all %>%
filter(condition != "CTRL")
cm.raw <- cm.raw.all %>%
.[, colnames(.) %in% rownames(meta)]
# set up project parameters
param_list <- initialize_params(ctypes_use = meta$ctypes %>% levels(),
ncores = 31,
rand_seed = 10)
# create project container
itd_container <- make_new_container(count_data=cm.raw,
meta_data=meta,
params=param_list,
label_donor_sex = F)## Warning in make_new_container(count_data = cm.raw, meta_data = meta, params =
## param_list, : Cell type names cannot have special characters '.', '_', or ':'.
## Removing these characters and concatenating name components.itd_container %<>% form_tensor(donor_min_cells=5,
norm_method='trim',
scale_factor=10000,
vargenes_method='norm_var_pvals',
vargenes_thresh=.05,
scale_var = TRUE,
var_scale_power = 0.5)## parsing data matrix by cell/tissue type...## cleaning data...## Keeping 19 donors. All donors have at least 5 cells in each cell type included.## collapsing count matrices from cells to donors (aka pseudobulk operation)...## normalizing data...## calculating gene overdispersion factors...## selecting highly variable genes from each cell type...## scaling variance...## forming tensor...## Complete!print(length(itd_container[["all_vargenes"]]))## [1] 701itd_container %<>% run_tucker_ica(ranks=c(6,7),
tucker_type = 'regular',
rotation_type = 'hybrid')
# get donor scores-metadata associations
itd_container %<>% get_meta_associations(vars_test=c('sex','pmi', "age", "brain_bank", "flowcell", "extract"),
stat_use='pval')
# plot donor scores
itd_container %<>% plot_donor_matrix(meta_vars=c('sex','pmi', "age", "brain_bank", "flowcell", "extract"),
show_donor_ids = TRUE,
add_meta_associations='pval')
# show the donor scores heatmap
p <- itd_container$plots$donor_matrix
p
Microglia, preparation
con.tmp <- qread("cao_micro_pvm.qs", nthreads = 10)$data.object
anno <- qread("anno_micro_pvm.qs")
cm.raw.all <- con.tmp$getJointCountMatrix(raw = T) %>%
Matrix::t() %>%
.[!grepl("MT-|RPS|RPL", rownames(.)), ]
meta.all <- con.tmp$getDatasetPerCell() %>%
data.frame(donors = .) %>%
mutate(ctypes = anno[rownames(.)] %>% unname()) %>%
.[complete.cases(.), ]
metadata.all <- read.delim("metadata.tsv")
for (cc in colnames(metadata.all)[-1]) {
meta.all[[cc]] <- metadata.all[[cc]][match(meta.all$donors, metadata.all$sample)]
}
# Convert integers to numeric
meta.all$age %<>% as.numeric()
meta.all$disease_duration %<>% as.numeric()CTRL vs MSA
meta <- meta.all %>%
filter(condition != "PD") %>%
mutate(donors = factor(donors),
ctypes = factor(ctypes))
metadata <- metadata.all %>%
filter(condition != "PD")
cm.raw <- cm.raw.all %>%
.[, colnames(.) %in% rownames(meta)]
# set up project parameters
param_list <- initialize_params(ctypes_use = meta$ctypes %>% levels(),
ncores = 31,
rand_seed = 10)
# create project container
itd_container <- make_new_container(count_data=cm.raw %>% .[, colnames(.) %in% rownames(meta)],
meta_data=meta,
params=param_list,
label_donor_sex = F)
itd_container %<>% form_tensor(donor_min_cells=3,
norm_method='trim',
scale_factor=10000,
vargenes_method='norm_var',
vargenes_thresh=500,
scale_var = TRUE,
var_scale_power = 0.5)## parsing data matrix by cell/tissue type...## cleaning data...## Keeping 10 donors. All donors have at least 3 cells in each cell type included.## collapsing count matrices from cells to donors (aka pseudobulk operation)...## normalizing data...## calculating gene overdispersion factors...## selecting highly variable genes from each cell type...## scaling variance...## forming tensor...## Complete!print(length(itd_container[["all_vargenes"]]))## [1] 1114itd_container %<>% run_tucker_ica(ranks=c(3,7),
tucker_type = 'regular',
rotation_type = 'hybrid')
factors <- c('sex','pmi', "age", "flowcell", "extract")
# get donor scores-metadata associations
itd_container %<>% get_meta_associations(vars_test = factors,
stat_use='pval')
# plot donor scores
itd_container %<>% plot_donor_matrix(meta_vars= factors,
show_donor_ids = TRUE,
add_meta_associations='pval')
# show the donor scores heatmap
p <- itd_container$plots$donor_matrix
p
CTRL vs PD
meta <- meta.all %>%
filter(condition != "MSA") %>%
mutate(donors = factor(donors),
ctypes = factor(ctypes))
metadata <- metadata.all %>%
filter(condition != "MSA")
cm.raw <- cm.raw.all %>%
.[, colnames(.) %in% rownames(meta)]
# set up project parameters
param_list <- initialize_params(ctypes_use = meta$ctypes %>% levels(),
ncores = 31,
rand_seed = 10)
# create project container
itd_container <- make_new_container(count_data=cm.raw,
meta_data=meta,
params=param_list,
label_donor_sex = F)
itd_container %<>% form_tensor(donor_min_cells=3,
norm_method='trim',
scale_factor=10000,
vargenes_method='norm_var',
vargenes_thresh=500,
scale_var = TRUE,
var_scale_power = 0.5)## parsing data matrix by cell/tissue type...## cleaning data...## Keeping 18 donors. All donors have at least 3 cells in each cell type included.## collapsing count matrices from cells to donors (aka pseudobulk operation)...## normalizing data...## calculating gene overdispersion factors...## selecting highly variable genes from each cell type...## scaling variance...## forming tensor...## Complete!print(length(itd_container[["all_vargenes"]]))## [1] 1012itd_container %<>% run_tucker_ica(ranks=c(4,10),
tucker_type = 'regular',
rotation_type = 'hybrid')
# get donor scores-metadata associations
itd_container %<>% get_meta_associations(vars_test=c('sex','pmi', "age", "brain_bank", "flowcell", "extract"),
stat_use='pval')
# plot donor scores
itd_container %<>% plot_donor_matrix(meta_vars=c('sex','pmi', "age", "brain_bank", "flowcell", "extract"),
show_donor_ids = TRUE,
add_meta_associations='pval')
# show the donor scores heatmap
p <- itd_container$plots$donor_matrix
p
PD vs MSA
meta <- meta.all %>%
filter(condition != "CTRL") %>%
mutate(donors = factor(donors),
ctypes = factor(ctypes))
metadata <- metadata.all %>%
filter(condition != "CTRL")
cm.raw <- cm.raw.all %>%
.[, colnames(.) %in% rownames(meta)]
# set up project parameters
param_list <- initialize_params(ctypes_use = meta$ctypes %>% levels(),
ncores = 31,
rand_seed = 10)
# create project container
itd_container <- make_new_container(count_data=cm.raw,
meta_data=meta,
params=param_list,
label_donor_sex = F)
itd_container %<>% form_tensor(donor_min_cells=3,
norm_method='trim',
scale_factor=10000,
vargenes_method='norm_var',
vargenes_thresh=500,
scale_var = TRUE,
var_scale_power = 0.5)## parsing data matrix by cell/tissue type...## cleaning data...## Keeping 14 donors. All donors have at least 3 cells in each cell type included.## collapsing count matrices from cells to donors (aka pseudobulk operation)...## normalizing data...## calculating gene overdispersion factors...## selecting highly variable genes from each cell type...## scaling variance...## forming tensor...## Complete!print(length(itd_container[["all_vargenes"]]))## [1] 1001itd_container %<>% run_tucker_ica(ranks=c(4,10),
tucker_type = 'regular',
rotation_type = 'hybrid')
# get donor scores-metadata associations
itd_container %<>% get_meta_associations(vars_test=c('sex','pmi', "age", "brain_bank", "flowcell", "extract"),
stat_use='pval')
# plot donor scores
itd_container %<>% plot_donor_matrix(meta_vars=c('sex','pmi', "age", "brain_bank", "flowcell", "extract"),
show_donor_ids = TRUE,
add_meta_associations='pval')
# show the donor scores heatmap
p <- itd_container$plots$donor_matrix
p
Oligodendrocytes, preparation
con.tmp <- qread("con_oligodendrocytes.qs", nthreads = 10)
anno <- qread("anno_oligo.qs")
cm.raw.all <- con.tmp$getJointCountMatrix(raw = T) %>%
.[rownames(.) %in% names(anno), !grepl("MT-|RPS|RPL", colnames(.))] %>%
Matrix::t()
meta.all <- con.tmp$getDatasetPerCell() %>%
data.frame(donors = .) %>%
mutate(ctypes = anno[rownames(.)] %>% unname()) %>%
.[complete.cases(.), ]
metadata.all <- read.delim("metadata.tsv")
for (cc in colnames(metadata.all)[-1]) {
meta.all[[cc]] <- metadata.all[[cc]][match(meta.all$donors, metadata.all$sample)]
}
# Convert integers to numeric
meta.all$age %<>% as.numeric()
meta.all$disease_duration %<>% as.numeric()CTRL vs MSA
meta <- meta.all %>%
filter(condition != "PD") %>%
mutate(donors = factor(donors),
ctypes = factor(ctypes))
metadata <- metadata.all %>%
filter(condition != "PD")
cm.raw <- cm.raw.all %>%
.[, colnames(.) %in% rownames(meta)]
# set up project parameters
param_list <- initialize_params(ctypes_use = meta$ctypes %>% levels(),
ncores = 31,
rand_seed = 10)
# create project container
itd_container <- make_new_container(count_data=cm.raw %>% .[, colnames(.) %in% rownames(meta)],
meta_data=meta,
params=param_list,
label_donor_sex = F)## Warning in make_new_container(count_data = cm.raw %>% .[, colnames(.) %in% :
## Cell type names cannot have special characters '.', '_', or ':'. Removing these
## characters and concatenating name components.itd_container %<>% form_tensor(donor_min_cells=5,
norm_method='trim',
scale_factor=10000,
vargenes_method='norm_var_pvals',
vargenes_thresh=0.05,
scale_var = TRUE,
var_scale_power = 1) # Diminishable effect at 0.5, but not on any other level## parsing data matrix by cell/tissue type...## cleaning data...## Keeping 17 donors. All donors have at least 5 cells in each cell type included.## collapsing count matrices from cells to donors (aka pseudobulk operation)...## normalizing data...## calculating gene overdispersion factors...## selecting highly variable genes from each cell type...## scaling variance...## forming tensor...## Complete!print(length(itd_container[["all_vargenes"]]))## [1] 1209itd_container %<>% run_tucker_ica(ranks=c(6,9),
tucker_type = 'regular',
rotation_type = 'hybrid')
# get donor scores-metadata associations
itd_container %<>% get_meta_associations(vars_test=c('sex','pmi', "age", "flowcell", "extract"), # Omitting brain bank
stat_use='pval')
# plot donor scores
itd_container %<>% plot_donor_matrix(meta_vars=c('sex','pmi', "age", "flowcell", "extract"), # Omitting brain bank
show_donor_ids = TRUE,
add_meta_associations='pval')
# show the donor scores heatmap
p <- itd_container$plots$donor_matrix
p
CTRL vs PD
meta <- meta.all %>%
filter(condition != "MSA") %>%
mutate(donors = factor(donors),
ctypes = factor(ctypes))
metadata <- metadata.all %>%
filter(condition != "MSA")
cm.raw <- cm.raw.all %>%
.[, colnames(.) %in% rownames(meta)]
# set up project parameters
param_list <- initialize_params(ctypes_use = meta$ctypes %>% levels(),
ncores = 31,
rand_seed = 10)
# create project container
itd_container <- make_new_container(count_data=cm.raw,
meta_data=meta,
params=param_list,
label_donor_sex = F)## Warning in make_new_container(count_data = cm.raw, meta_data = meta, params =
## param_list, : Cell type names cannot have special characters '.', '_', or ':'.
## Removing these characters and concatenating name components.itd_container %<>% form_tensor(donor_min_cells=5,
norm_method='trim',
scale_factor=10000,
vargenes_method='norm_var_pvals',
vargenes_thresh=.05,
scale_var = TRUE,
var_scale_power = 0.5)## parsing data matrix by cell/tissue type...## cleaning data...## Keeping 21 donors. All donors have at least 5 cells in each cell type included.## collapsing count matrices from cells to donors (aka pseudobulk operation)...## normalizing data...## calculating gene overdispersion factors...## selecting highly variable genes from each cell type...## scaling variance...## forming tensor...## Complete!print(length(itd_container[["all_vargenes"]]))## [1] 1505itd_container %<>% run_tucker_ica(ranks=c(4,8),
tucker_type = 'regular',
rotation_type = 'hybrid')
# get donor scores-metadata associations
itd_container %<>% get_meta_associations(vars_test=c('sex','pmi', "age", "brain_bank", "flowcell", "extract"),
stat_use='pval')
# plot donor scores
itd_container %<>% plot_donor_matrix(meta_vars=c('sex','pmi', "age", "brain_bank", "flowcell", "extract"),
show_donor_ids = TRUE,
add_meta_associations='pval')
# show the donor scores heatmap
p <- itd_container$plots$donor_matrix
p
PD vs MSA
meta <- meta.all %>%
filter(condition != "CTRL") %>%
mutate(donors = factor(donors),
ctypes = factor(ctypes))
metadata <- metadata.all %>%
filter(condition != "CTRL")
cm.raw <- cm.raw.all %>%
.[, colnames(.) %in% rownames(meta)]
# set up project parameters
param_list <- initialize_params(ctypes_use = meta$ctypes %>% levels(),
ncores = 31,
rand_seed = 10)
# create project container
itd_container <- make_new_container(count_data=cm.raw,
meta_data=meta,
params=param_list,
label_donor_sex = F)## Warning in make_new_container(count_data = cm.raw, meta_data = meta, params =
## param_list, : Cell type names cannot have special characters '.', '_', or ':'.
## Removing these characters and concatenating name components.itd_container %<>% form_tensor(donor_min_cells=5,
norm_method='trim',
scale_factor=10000,
vargenes_method='norm_var_pvals',
vargenes_thresh=.05,
scale_var = TRUE,
var_scale_power = 1) # Diminishable effect at 0.5, but not on any other level## parsing data matrix by cell/tissue type...## cleaning data...## Keeping 18 donors. All donors have at least 5 cells in each cell type included.## collapsing count matrices from cells to donors (aka pseudobulk operation)...## normalizing data...## calculating gene overdispersion factors...## selecting highly variable genes from each cell type...## scaling variance...## forming tensor...## Complete!print(length(itd_container[["all_vargenes"]]))## [1] 1146itd_container %<>% run_tucker_ica(ranks=c(5,6),
tucker_type = 'regular',
rotation_type = 'hybrid')
# get donor scores-metadata associations
itd_container %<>% get_meta_associations(vars_test=c('sex','pmi', "age", "brain_bank", "flowcell", "extract"),
stat_use='pval')
# plot donor scores
itd_container %<>% plot_donor_matrix(meta_vars=c('sex','pmi', "age", "brain_bank", "flowcell", "extract"),
show_donor_ids = TRUE,
add_meta_associations='pval')
# show the donor scores heatmap
p <- itd_container$plots$donor_matrix
p
OPCs, preparation
con.tmp <- qread("con_opcs.qs", nthreads = 10)
anno <- qread("anno_opc.qs")
cm.raw.all <- con.tmp$getJointCountMatrix(raw = T) %>%
Matrix::t() %>%
.[!grepl("MT-|RPS|RPL", rownames(.)), ]
meta.all <- con.tmp$getDatasetPerCell() %>%
data.frame(donors = .) %>%
mutate(ctypes = anno[rownames(.)] %>% unname()) %>%
.[complete.cases(.), ]
metadata.all <- read.delim("metadata.tsv")
for (cc in colnames(metadata.all)[-1]) {
meta.all[[cc]] <- metadata.all[[cc]][match(meta.all$donors, metadata.all$sample)]
}
# Convert integers to numeric
meta.all$age %<>% as.numeric()
meta.all$disease_duration %<>% as.numeric()CTRL vs MSA
meta <- meta.all %>%
filter(condition != "PD") %>%
mutate(donors = factor(donors),
ctypes = factor(ctypes))
metadata <- metadata.all %>%
filter(condition != "PD")
cm.raw <- cm.raw.all %>%
.[, colnames(.) %in% rownames(meta)]
# set up project parameters
param_list <- initialize_params(ctypes_use = meta$ctypes %>% levels(),
ncores = 31,
rand_seed = 10)
# create project container
itd_container <- make_new_container(count_data=cm.raw %>% .[, colnames(.) %in% rownames(meta)],
meta_data=meta,
params=param_list,
label_donor_sex = F)
itd_container %<>% form_tensor(donor_min_cells=5,
norm_method='trim',
scale_factor=10000,
vargenes_method='norm_var_pvals',
vargenes_thresh=0.2,
scale_var = TRUE,
var_scale_power = 0.5) # Diminishable effect at 2, but not on any other level## parsing data matrix by cell/tissue type...## cleaning data...## Keeping 17 donors. All donors have at least 5 cells in each cell type included.## collapsing count matrices from cells to donors (aka pseudobulk operation)...## normalizing data...## calculating gene overdispersion factors...## selecting highly variable genes from each cell type...## scaling variance...## forming tensor...## Complete!print(length(itd_container[["all_vargenes"]]))## [1] 778itd_container %<>% run_tucker_ica(ranks=c(4,5),
tucker_type = 'regular',
rotation_type = 'hybrid')
# get donor scores-metadata associations
itd_container %<>% get_meta_associations(vars_test=c('sex','pmi', "age", "flowcell", "extract"), # Omitting brain bank
stat_use='pval')
# plot donor scores
itd_container %<>% plot_donor_matrix(meta_vars=c('sex','pmi', "age", "flowcell", "extract"), # Omitting brain bank
show_donor_ids = TRUE,
add_meta_associations='pval')
# show the donor scores heatmap
p <- itd_container$plots$donor_matrix
p
CTRL vs PD
meta <- meta.all %>%
filter(condition != "MSA") %>%
mutate(donors = factor(donors),
ctypes = factor(ctypes))
metadata <- metadata.all %>%
filter(condition != "MSA")
cm.raw <- cm.raw.all %>%
.[, colnames(.) %in% rownames(meta)]
# set up project parameters
param_list <- initialize_params(ctypes_use = meta$ctypes %>% levels(),
ncores = 31,
rand_seed = 10)
# create project container
itd_container <- make_new_container(count_data=cm.raw,
meta_data=meta,
params=param_list,
label_donor_sex = F)
itd_container %<>% form_tensor(donor_min_cells=5,
norm_method='trim',
scale_factor=10000,
vargenes_method='norm_var_pvals',
vargenes_thresh=.4,
scale_var = TRUE,
var_scale_power = 1) # No effect at 0.5, but 1, 1.5, 2## parsing data matrix by cell/tissue type...## cleaning data...## Keeping 21 donors. All donors have at least 5 cells in each cell type included.## collapsing count matrices from cells to donors (aka pseudobulk operation)...## normalizing data...## calculating gene overdispersion factors...## selecting highly variable genes from each cell type...## scaling variance...## forming tensor...## Complete!print(length(itd_container[["all_vargenes"]]))## [1] 833itd_container %<>% run_tucker_ica(ranks=c(4,6),
tucker_type = 'regular',
rotation_type = 'hybrid')
# get donor scores-metadata associations
itd_container %<>% get_meta_associations(vars_test=c('sex','pmi', "age", "brain_bank", "flowcell", "extract"),
stat_use='pval')
# plot donor scores
itd_container %<>% plot_donor_matrix(meta_vars=c('sex','pmi', "age", "brain_bank", "flowcell", "extract"),
show_donor_ids = TRUE,
add_meta_associations='pval')
# show the donor scores heatmap
p <- itd_container$plots$donor_matrix
p
PD vs MSA
meta <- meta.all %>%
filter(condition != "CTRL") %>%
mutate(donors = factor(donors),
ctypes = factor(ctypes))
metadata <- metadata.all %>%
filter(condition != "CTRL")
cm.raw <- cm.raw.all %>%
.[, colnames(.) %in% rownames(meta)]
# set up project parameters
param_list <- initialize_params(ctypes_use = meta$ctypes %>% levels(),
ncores = 31,
rand_seed = 10)
# create project container
itd_container <- make_new_container(count_data=cm.raw,
meta_data=meta,
params=param_list,
label_donor_sex = F)
itd_container %<>% form_tensor(donor_min_cells=5,
norm_method='trim',
scale_factor=10000,
vargenes_method='norm_var_pvals',
vargenes_thresh=.4,
scale_var = TRUE,
var_scale_power = 0.5)## parsing data matrix by cell/tissue type...## cleaning data...## Keeping 18 donors. All donors have at least 5 cells in each cell type included.## collapsing count matrices from cells to donors (aka pseudobulk operation)...## normalizing data...## calculating gene overdispersion factors...## selecting highly variable genes from each cell type...## scaling variance...## forming tensor...## Complete!print(length(itd_container[["all_vargenes"]]))## [1] 617itd_container %<>% run_tucker_ica(ranks=c(5,6),
tucker_type = 'regular',
rotation_type = 'hybrid')
# get donor scores-metadata associations
itd_container %<>% get_meta_associations(vars_test=c('sex','pmi', "age", "brain_bank", "flowcell", "extract"),
stat_use='pval')
# plot donor scores
itd_container %<>% plot_donor_matrix(meta_vars=c('sex','pmi', "age", "brain_bank", "flowcell", "extract"),
show_donor_ids = TRUE,
add_meta_associations='pval')
# show the donor scores heatmap
p <- itd_container$plots$donor_matrix
p
## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 18930383 1011.0 39451381 2107.0 39451381 2107.0
## Vcells 1088563262 8305.1 3171389962 24195.8 9054649287 69081.5Extended Data Figure 6
For panels a-d, see scCODA.ipynb.
Load data
anno.major <- qread("anno_major.qs")
meta <- read.delim("metadata.tsv", sep = "\t")
anno.micro <- qread("anno_micro_pvm.qs") %>%
renameAnnotation("Steady-state","MIC_steady-state") %>%
renameAnnotation("Intermediate1","MIC_intermediate1") %>%
renameAnnotation("Intermediate2","MIC_intermediate2") %>%
renameAnnotation("Activated","MIC_activated")
anno.glia <- c("anno_astro.qs",
"anno_oligo.qs",
"anno_opc.qs") %>%
lapply(qread) %>%
Reduce(c, .) %>%
factor() %>%
renameAnnotation("Homeostatic_astrocytes", "AS_homeostatic") %>%
renameAnnotation("Reactive_astrocytes", "AS_reactive") %>%
renameAnnotation("Homeostatic_LINC01608", "OL_LINC01608") %>%
renameAnnotation("Homeostatic_SLC5A11", "OL_SLC5A11") %>%
renameAnnotation("Reactive_SCGZ", "OL_SGCZ")
anno.neurons <- qread("anno_neurons.qs")6e
# Minor final
anno.minor <- anno.major[!anno.major %in% c("PVMs", "Astrocytes", "Oligodendrocytes", "OPCs", "Microglia")] %>%
{factor(c(., anno.glia, anno.micro))} %>%
factor(levels = sort(levels(.)))
df <- data.frame(cid = names(anno.minor), anno = unname(anno.minor)) %>%
mutate(sample = strsplit(cid, "!!") %>% sget(1)) %>%
group_by(sample, anno) %>%
summarize(n = n()) %>%
mutate(prop = n / sum(n) * 100) %>%
ungroup() %>%
filter(anno %in% c("Inh. neurons", "Exc. neurons", "MSN")) %>%
mutate(age = meta$age[match(sample, meta$sample)]) %>%
mutate(age_bin = cut(age, breaks = c(0, 60, 70, 80, 90), labels = c("0-60", "60-70", "70-80", "80-90")),
condition = grepl.replace(sample, c("CTRL", "MSA", "PD")))## `summarise()` has regrouped the output.
## ℹ Summaries were computed grouped by sample and anno.
## ℹ Output is grouped by sample.
## ℹ Use `summarise(.groups = "drop_last")` to silence this message.
## ℹ Use `summarise(.by = c(sample, anno))` for per-operation grouping
## (`?dplyr::dplyr_by`) instead.df %>%
ggplot(aes(age_bin, prop)) +
geom_boxplot() +
geom_point(position = position_dodge(width = 0.85)) +
theme_bw() +
facet_wrap(~ anno) -> p
p
Statistics
df %>%
group_by(anno) %>%
rstatix::kruskal_test(prop ~ age_bin) %>%
mutate(padj = p.adjust(p, method = "BH"))## # A tibble: 3 × 8
## anno .y. n statistic df p method padj
## <fct> <chr> <int> <dbl> <int> <dbl> <chr> <dbl>
## 1 Exc. neurons prop 15 0.788 3 0.852 Kruskal-Wallis 0.852
## 2 Inh. neurons prop 24 5.23 3 0.156 Kruskal-Wallis 0.376
## 3 MSN prop 24 4.10 3 0.251 Kruskal-Wallis 0.3766f
# Minor final
anno.minor <- anno.major[!anno.major %in% c("Inh. neurons", "MSN", "Exc. neurons", "PVMs", "Astrocytes", "Oligodendrocytes", "OPCs", "Microglia")] %>%
{factor(c(., anno.neurons, anno.glia, anno.micro))} %>%
factor(levels = sort(levels(.)))
df <- data.frame(cid = names(anno.minor), anno = unname(anno.minor)) %>%
mutate(sample = strsplit(cid, "!!") %>% sget(1)) %>%
group_by(sample, anno) %>%
summarize(n = n()) %>%
mutate(prop = n / sum(n) * 100) %>%
ungroup() %>%
filter(anno %in% levels(anno.micro)) %>%
mutate(age = meta$age[match(sample, meta$sample)]) %>%
mutate(age_bin = cut(age, breaks = c(0, 60, 70, 80, 90), labels = c("0-60", "60-70", "70-80", "80-90")),
condition = grepl.replace(sample, c("CTRL", "MSA", "PD")))## `summarise()` has regrouped the output.
## ℹ Summaries were computed grouped by sample and anno.
## ℹ Output is grouped by sample.
## ℹ Use `summarise(.groups = "drop_last")` to silence this message.
## ℹ Use `summarise(.by = c(sample, anno))` for per-operation grouping
## (`?dplyr::dplyr_by`) instead.df %>%
ggplot(aes(age_bin, prop)) +
geom_boxplot() +
geom_point(position = position_dodge(width = 0.85)) +
theme_bw() +
facet_wrap(~ anno) -> p
p
Statistics
df %>%
group_by(anno) %>%
rstatix::kruskal_test(prop ~ age_bin) %>%
mutate(padj = p.adjust(p, method = "BH"))## # A tibble: 5 × 8
## anno .y. n statistic df p method padj
## <fct> <chr> <int> <dbl> <int> <dbl> <chr> <dbl>
## 1 MIC_activated prop 29 7.58 3 0.0556 Kruskal-Wallis 0.139
## 2 MIC_intermediate1 prop 29 4.58 3 0.206 Kruskal-Wallis 0.258
## 3 MIC_intermediate2 prop 29 8.94 3 0.0302 Kruskal-Wallis 0.139
## 4 MIC_steady-state prop 29 2.78 3 0.427 Kruskal-Wallis 0.427
## 5 PVMs prop 29 5.13 3 0.163 Kruskal-Wallis 0.258Extended Data Figure 7
We’re calculating cosine similarities to annotated data from Garma et al.. The data are available here.
Prepare data
Here, we’re showing how we prepared the data. This will not be run again.
lp.raw <- loomR::connect("Interneurons_raw.loom", mode = "r+", skip.validate = T)
cm.raw <- lp.raw[["matrix"]][,] %>%
`dimnames<-`(list(
lp.raw[["col_attrs/obs_names"]][],
lp.raw[["row_attrs/var_names"]][]
))
lp.raw$close()
lp.norm <- loomR::connect("Interneurons_processed.loom", mode = "r+", skip.validate = T)
anno <- setNames(
lp.norm$col.attrs$`IN subclass`[],
lp.norm$col.attrs$obs_names[]
) %>%
factor()
spc <- setNames(
lp.norm$col.attrs$sample[],
lp.norm$col.attrs$obs_names[]
) %>%
factor()
origin <- setNames(
lp.norm$col.attrs$Origin[],
lp.norm$col.attrs$obs_names[]
) %>%
factor()
area <- setNames(
lp.norm$col.attrs$Region[],
lp.norm$col.attrs$obs_names[]
) %>%
factor()
sex <- setNames(
lp.norm$col.attrs$Sex[],
lp.norm$col.attrs$obs_names[]
) %>%
factor()
umap <- `rownames<-`(lp.norm$col.attrs$X_umap[,] %>% t(),
lp.norm$col.attrs$obs_names[])
lp.norm$close()Then, we need to prepare a Conos object of those data. This will not be run here.
preprocessed <- spc %>%
split(names(.), .) %>%
.[sapply(., length) > 50] %>%
lapply(\(cid) cm.raw[cid, ]) %>%
lapply(Matrix::t) %>%
lapply(basicP2proc, get.largevis = F, get.tsne = F, make.geneknn = F, n.cores = 20)
con <- Conos$new(preprocessed, n.cores = 32)
con$embeddings$PCA$UMAP <- umap
con$embedding <- umap
con$clusters$leiden$groups <- anno
con$clusters$sex$groups <- sex
con$clusters$area$groups <- area
con$clusters$origin$groups <- origin
con$clusters$spc$groups <- spc
qsave(con, "con_garma.qs", nthreads = 10)7a
# Our data
rydbirk.neurons.anno <- qread("anno_neurons.qs") %>%
factor(labels = paste("Rydbirk", levels(.), sep = "-"))
rydbirk.neurons <- qread("cao_neurons.qs", nthreads = 10)$data.object
rydbirk.neurons.pseudo <- rydbirk.neurons$getJointCountMatrix(raw = T) %>%
sccore::collapseCellsByType(groups = rydbirk.neurons.anno, min.cell.count = 1) %>%
t() %>%
apply(2, as, "integer")
rydbirk.neurons.pseudo %<>%
DESeq2::DESeqDataSetFromMatrix(.,
colnames(.) %>%
strsplit("-") %>%
sget(1) %>%
data.frame() %>%
`dimnames<-`(list(colnames(rydbirk.neurons.pseudo), "group")),
design = ~ 1) %>%
DESeq2::estimateSizeFactors() %>%
DESeq2::counts(normalized = T)
# Garma data
con.garma <- qread("con_garma.qs", nthreads = 10)
garma.neurons.anno <- con.garma$clusters$anno$groups %>%
factor() %>%
factor(labels = paste("Garma", levels(.), sep = "-"))
garma.neurons.pseudo <- con.garma$getJointCountMatrix(raw = T) %>%
sccore::collapseCellsByType(groups = garma.neurons.anno, min.cell.count = 1) %>%
t() %>%
apply(2, as, "integer")
garma.neurons.pseudo %<>%
DESeq2::DESeqDataSetFromMatrix(.,
colnames(.) %>%
strsplit("_") %>%
sget(1) %>%
data.frame() %>%
`dimnames<-`(list(colnames(garma.neurons.pseudo), "group")),
design = ~ 1) %>%
DESeq2::estimateSizeFactors() %>%
DESeq2::counts(normalized = T)
# We rotate Garma into Rydbirk sample PCA space
cm.rydbirk <- rydbirk.neurons.pseudo %>%
as.data.frame() %>%
filter(rowSums(.) > 0)
cm.garma <- garma.neurons.pseudo %>%
as.data.frame() %>%
filter(rowSums(.) > 0)
genesToKeep <- conos:::getOdGenesUniformly(append(con.garma$samples, rydbirk.neurons$samples), 50) %>%
intersect(rownames(cm.rydbirk)) %>%
intersect(rownames(cm.garma))
pc.res <-
cm.rydbirk[genesToKeep, ] %>%
t() %>%
prcomp(center = T,
scale = T)
pc.tmp <- cm.garma[genesToKeep, ] %>%
as.data.frame() %>%
filter(rowSums(.) > 0) %>%
t() %>%
scale(pc.res$center, pc.res$scale) %*% pc.res$rotationdat.plot <- rbind(pc.res$x, pc.tmp) %>%
data.frame() %>%
mutate(id = rownames(.)) %>%
mutate(study = ifelse(grepl("Rydbirk", id), "Rydbirk", "Garma") %>% factor(),
anno = strsplit(id, "-") %>% sget(2))
lsa::cosine(dat.plot %>%
select(-id, -study, -anno) %>%
t()) %>%
reshape2::melt() %>%
ggplot(aes(Var1, Var2, fill = value)) +
geom_tile() +
scale_fill_gradient2(low = "skyblue3", mid = "white", high = "deeppink3") +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
labs(title = "Top 50 OD genes, Garma PCA rotation into Rydbirk PCA space")
7b
cm.garma.raw <- con.garma$getJointCountMatrix(raw = T)
cm.rydbirk.raw <- rydbirk.neurons$getJointCountMatrix(raw = T)
idx <- intersect(colnames(cm.garma.raw), colnames(cm.rydbirk.raw))
cm.garma.ext <- cm.garma.raw %>%
sccore:::extendMatrix(idx)
cm.rydbirk.ext <- cm.rydbirk.raw %>%
sccore:::extendMatrix(idx)
cm.comb <- rbind(cm.garma.ext, cm.rydbirk.ext)
anno.comb <- c(garma.neurons.anno %>% .[names(.) %in% rownames(cm.comb)], rydbirk.neurons.anno %>% .[names(.) %in% rownames(cm.comb)]) %>%
factor()
cm.comb %<>% .[rownames(.) %in% names(anno.comb), ]
unique(
c("GAD1","GAD2","PPP1R1B","SLC17A7","CHAT","CALB2","DCC","ID2","MEIS2","ST18","NKX2-1","NR2F2","PAX6","PVALB","RXFP1","SST","VIP","RELN","SATB2","DRD1","DRD2","FOXP2", "LRP8", "VLDLR") %>%
c("CCK", "VIP", "CXCL14", "CHAT", "PTHLH", "MOXD1", "CHST9", "TAC3", "SST", "NPY", "PVALB", "GRIK3", "DACH1", "GAD1", "GAD2")
) %>%
sccore::dotPlot(cm.comb, anno.comb) + scale_color_gradient(low = "grey80", high = "firebrick")## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 20307595 1084.6 39451381 2107.0 39451381 2107.0
## Vcells 1512121776 11536.6 3171389962 24195.8 9054649287 69081.5Extended Data Figure 8
Prepare data
We’re calculating cosine similarities to annotated data from Kamath et al.. The data are available here.
cm <- Matrix::readMM("GSE178265_Homo_matrix.mtx") %>%
`dimnames<-`(list(
read.delim("GSE178265_Homo_features.tsv", header = F)$V1,
read.delim("GSE178265_Homo_bcd.tsv", header = F)$V1
))
metadata.raw <- read.delim("METADATA_PD.tsv", header = T)[-1, ]
cells.to.omit <- metadata.raw %>%
filter(donor_id %in% c("NIH1", "Rat1", "TreeShrew1")) %>%
pull(NAME)
meta.per.cell <- metadata.raw %>%
filter(!NAME %in% cells.to.omit)
meta.per.sample <- metadata.raw %>%
filter(!NAME %in% cells.to.omit) %>%
select(-NAME, -libname, -biosample_id) %>%
mutate(area = grepl.replace(organ__ontology_label, c("substantia nigra pars compacta", "caudate nucleus"), c("SN", "CN"))) %>%
mutate(donor_area = paste(donor_id, area, sep = "-")) %>%
.[match(unique(.$donor_area), .$donor_area), ]
cids.per.donor.area <- meta.per.cell %>%
mutate(area = grepl.replace(organ__ontology_label, c("substantia nigra pars compacta", "caudate nucleus"), c("SN", "CN"))) %>%
mutate(donor_area = paste(donor_id, area, sep = "-")) %$%
split(NAME, donor_area)
cm.list <- meta %>%
sccore::plapply(\(mm) cm[, colnames(cm) %in% rownames(mm)], n.cores = 7)
cm.area.donor <- cm.list %>%
lapply(\(area) sccore::plapply(cids.per.donor.area, \(cid) area[, colnames(area) %in% cid], n.cores = 22)) %>%
lapply(\(cms) cms[sapply(cms, ncol) > 30]) %>%
lapply(lapply, as, "CsparseMatrix") %>%
lapply(lapply, basicP2proc, get.largevis = F, get.tsne = F, make.geneknn = F, n.cores = 20)# Annotations
embeddings <- dir(pattern = "UMAP")
embeddings %<>%
lapply(\(type) read.delim(type, header = T, row.names = 1)) %>%
setNames(embeddings %>% strsplit("_") %>% sget(1)) %>%
lapply(dplyr::rename, subtype = Cell_Type)
con.list <- names(embeddings) %>%
lapply(\(nn) {
con <- Conos$new(cm.area.donor[[nn]], n.cores = 32)
con$embeddings <- list(UMAP = embeddings[[nn]][, -3])
con$embedding <- con$embeddings[[1]]
con$clusters <- list(leiden = list(groups = embeddings[[nn]] %>% {setNames(pull(., subtype), rownames(.))}))
return(con)
}) %>%
setNames(names(embeddings))## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 22781884 1216.7 39451381 2107 39451381 2107.0
## Vcells 6139172459 46838.2 8075595639 61612 9054649287 69081.5Plot
# Our data
rydbirk.neurons.anno <- qread("anno_neurons.qs") %>%
factor(labels = paste("Rydbirk", levels(.), sep = "-"))
rydbirk.neurons <- qread("cao_neurons.qs", nthreads = 10)$data.object
rydbirk.neurons.pseudo <- rydbirk.neurons$getJointCountMatrix(raw = T) %>%
sccore::collapseCellsByType(groups = rydbirk.neurons.anno, min.cell.count = 1) %>%
t() %>%
apply(2, as, "integer")
rydbirk.neurons.pseudo %<>%
DESeq2::DESeqDataSetFromMatrix(.,
colnames(.) %>%
strsplit("-") %>%
sget(1) %>%
data.frame() %>%
`dimnames<-`(list(colnames(rydbirk.neurons.pseudo), "group")),
design = ~ 1) %>%
DESeq2::estimateSizeFactors() %>%
DESeq2::counts(normalized = T)
# Kamath data
kamath.neurons.anno1 <- con.list$da$clusters$leiden$groups %>%
factor() %>%
factor(labels = paste("Kamath", levels(.), sep = "-"))
kamath.neurons.anno2 <- con.list$nonda$clusters$leiden$groups %>%
factor() %>%
factor(labels = paste("Kamath", levels(.), sep = "-"))
kamath.neurons.anno <- factor(c(kamath.neurons.anno1, kamath.neurons.anno2))
kamath.cm1 <- con.list$da$getJointCountMatrix(raw = T)
kamath.cm2 <- con.list$nonda$getJointCountMatrix(raw = T)
kamath.neurons.pseudo <- rbind(kamath.cm1, kamath.cm2) %>%
sccore::collapseCellsByType(groups = kamath.neurons.anno, min.cell.count = 1) %>%
t() %>%
apply(2, as, "integer")
kamath.neurons.pseudo %<>%
DESeq2::DESeqDataSetFromMatrix(.,
colnames(.) %>%
strsplit("_") %>%
sget(1) %>%
data.frame() %>%
`dimnames<-`(list(colnames(kamath.neurons.pseudo), "group")),
design = ~ 1) %>%
DESeq2::estimateSizeFactors() %>%
DESeq2::counts(normalized = T)
# We rotate Kamath into Rydbirk sample PCA space
cm.rydbirk <- rydbirk.neurons.pseudo %>%
as.data.frame() %>%
filter(rowSums(.) > 0)
cm.kamath <- kamath.neurons.pseudo %>%
as.data.frame() %>%
filter(rowSums(.) > 0)genesToKeep <- conos:::getOdGenesUniformly(append(con.list$da$samples, rydbirk.neurons$samples) %>% append(con.list$nonda$samples), 100) %>%
intersect(rownames(cm.rydbirk)) %>%
intersect(rownames(cm.kamath))
pc.res <-
cm.rydbirk[genesToKeep, ] %>%
t() %>%
prcomp(center = T,
scale = T)
pc.tmp <- cm.kamath[genesToKeep, ] %>%
as.data.frame() %>%
filter(rowSums(.) > 0) %>%
t() %>%
scale(pc.res$center, pc.res$scale) %*% pc.res$rotation
# Plot
dat.plot <- rbind(pc.res$x, pc.tmp) %>%
data.frame() %>%
mutate(id = rownames(.)) %>%
mutate(study = ifelse(grepl("Rydbirk", id), "Rydbirk", "Kamath") %>% factor(),
anno = strsplit(id, "-") %>% sget(2))
lsa::cosine(dat.plot %>%
select(-id, -study, -anno) %>%
t()) %>%
reshape2::melt() %>%
ggplot(aes(Var1, Var2, fill = value)) +
geom_tile() +
scale_fill_gradient2(low = "skyblue3", mid = "white", high = "deeppink3") +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
labs(title = "Top 100 OD genes, Kamath PCA rotation into Rydbirk PCA space")
## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 21443376 1145.2 39451381 2107.0 39451381 2107.0
## Vcells 5948867367 45386.3 11629033719 88722.5 11286418961 86108.6Extended Data Figure 9
Here, we use public data from Smajic et al.. We isolated neurons and used the available annotation (IPDCO_hg_midbrain_cell.tsv).
con.smajic <- qread("smajic_neurons.qs", nthreads = 10)
con.neurons <- qread("cao_neurons.qs", nthreads = 10)$data.object
# Get Smajic annotation
tmp <- data.frame(cid = rownames(con.smajic$embedding)) %>%
mutate(barcode = strsplit(cid, "_") %>%
sget(3))
anno <- data.table::fread("IPDCO_hg_midbrain_cell.tsv", header = T) %>%
mutate(barcode = strsplit(barcode, "_") %>%
sget(1)) %>%
mutate(cid = tmp$cid[match(barcode, tmp$barcode)]) %>%
filter(!is.na(cid)) %>%
pull(cell_ontology, cid) %>%
.[!. %in% c("Astrocytes", "Endothelial cells", "Ependymal", "Microglia", "Oligodendrocytes", "OPCs", "Pericytes")] %>%
factor()
cowplot::plot_grid(plotlist = list(
con.smajic$plotGraph(groups = anno, title = "Smajic neurons, annotation", font.size = 3, shuffle.colors = T, plot.na = F) + theme(line = element_blank()),
con.neurons$plotGraph(groups = anno.neurons, title = "Rydbirk neurons, annotation", font.size = 3) + theme(line = element_blank()),
con.smajic$plotGraph(groups = anno, gene = "RELN", title = "RELN expression", plot.na = F) + theme(line = element_blank()),
con.neurons$plotGraph(gene = "RELN", title = "RELN expression", plot.na = F) + theme(line = element_blank()),
con.smajic$plotGraph(groups = anno, gene = "CADPS2", title = "CADPS2 expression", plot.na = F) + theme(line = element_blank()),
con.neurons$plotGraph(gene = "CADPS2", title = "CADPS2 expression", plot.na = F) + theme(line = element_blank())),
nrow = 3)
## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 21578074 1152.4 39451381 2107.0 39451381 2107.0
## Vcells 5949818482 45393.6 11629033719 88722.5 11286418961 86108.6Extended Data Figure 10
These plots were created with results from LIANA in Python.
Extended Data Figure 11
cm.merged <- con$getJointCountMatrix(raw = T) %>%
Matrix::t() %>%
.[, colnames(.) %in% names(anno.major)]
# Create sample-wise annotation
anno.donor <- con$getDatasetPerCell()[colnames(cm.merged)]
anno.subtype <- anno.major %>%
.[!is.na(.)] %>%
factor()
idx <- intersect(anno.donor %>% names(), anno.subtype %>% names())
anno.donor %<>% .[idx]
anno.subtype %<>% .[idx] %>%
{`names<-`(as.character(.), names(.))}
anno.final <- paste0(anno.donor,"!!",anno.subtype) %>%
`names<-`(anno.donor %>% names())
# Create pseudo CM
cm.pseudo <- sccore::collapseCellsByType(cm.merged %>% Matrix::t(),
groups = anno.final, min.cell.count = 1) %>%
t() %>%
apply(2, as, "integer")
cm.pseudo %<>%
{. + 1} %>%
DESeq2::DESeqDataSetFromMatrix(.,
colnames(.) %>%
strsplit("_") %>%
sget(1) %>%
data.frame() %>%
`dimnames<-`(list(colnames(cm.pseudo), "group")),
design = ~ group) %>%
DESeq2::estimateSizeFactors() %>%
DESeq2::counts(normalized = T)## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factorsc("TSPO", "COQ2", "MAPT", "FBXO47", "ELOVL7", "EDN1", "GAB1", "TENM2", "RABGEF1", "PLA2G4C", "INPP4B", "ZIC1", "ZIC2", "ZIC3", "ZIC4", "SNCA", "TPPP", "TPPP") %>%
{Map(\(gene, leg) plotGenePseudoBulk(gene, cm.pseudo, leg), gene = ., leg = c(rep(F, 17), T))} %>%
cowplot::plot_grid(plotlist = ., ncol = 3) -> p
p
Extended Data Figures 12 & 13
Load and prepare data
cao <- qread("cao_micro_pvm.qs", nthreads = 10)
cao$plot.theme <- theme_bw()
anno.sort <- anno.micro[rownames(cao$data.object$embedding)]
anno.sel <- anno.sort[!anno.sort %in% c("MIC_intermediate1", "PVMs")] %>% factor()
emb <- cao$data.object$embedding %>%
`colnames<-`(c("UMAP1","UMAP2")) %>%
.[rownames(.) %in% names(anno.sel), ]
sds_obj <- slingshot(emb,
anno.sel,
start.clus = "MIC_steady-state",
stretch = 0
)
sds <- as.SlingshotDataSet(sds_obj)
pseudotime <- sds_obj@assays@data@listData$pseudotime[, 1]12a
plot.df <- cao$data.object$embedding %>%
as.data.frame() %>%
.[rownames(.) %in% names(sds_obj), ] %>%
`colnames<-`(c("UMAP1","UMAP2")) %>%
mutate(., annotation = anno.micro[rownames(.)] %>% as.factor()) %>%
.[complete.cases(.),]
ldata <- getTscanTrajectory(cao$data.object, anno.sel)
cao$data.object$embedding %>%
as.data.frame() %>%
mutate(., unname(anno.micro[rownames(.)])) %>%
setNames(c("UMAP1", "UMAP2", "annotation")) %>%
mutate(., pseudotime = pseudotime[rownames(.)]) %>%
ggplot() +
geom_point(aes(UMAP1, UMAP2, col = pseudotime), size = 0.3) +
geom_line(data = ldata1, aes(UMAP1, UMAP2, group = edge), size = 1) +
theme_bw() +
theme(line = element_blank()) +
scale_color_gradient(low = "navyblue", high = "orange") +
labs(col = "Pseudotime", x = "UMAP1", y = "UMAP2")# +## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
12b
Here, we show the code for running the generalized additive mixed
model. It takes a significant amount of time to run, we provide data in
pseudotime_micro_l1.qs.
mat <- cao$data.object$getJointCountMatrix() %>%
.[match(names(sds_obj), rownames(.)), colSums(.) > 2e2] %>%
.[, !grepl(pattern = "RPL|RPS|MT-", colnames(.))]
mt <- mat %>%
as.matrix() %>%
as.data.frame() %>%
tibble::rownames_to_column() %>%
{message("Mutating"); mutate(.,
pseudotime = pseudotime[rowname],
condition = cao$data.object$getDatasetPerCell()[rowname] %>%
as.character() %>%
strsplit("_") %>%
sget(1),
sample = strsplit(rowname, "!!") %>%
sget(1))} %>%
.[complete.cases(.), ] %>%
{message("Melting"); reshape2::melt(., id.vars = c("rowname", "pseudotime", "condition", "sample"))} %$%
{message("Splitting"); split(., variable)}res <- mt %>%
sccore::plapply(\(x) {
fit_full <- gamm4(data = x, formula = value ~ s(pseudotime, by = factor(condition)), random = ~(1 | sample), REML = F)
fit_reduced <- gamm4(data = x, formula = value ~ s(pseudotime), random = ~(1 | sample), REML = F)
fit_none <- gamm4(data = x, formula = value ~ 1, random = ~(1 | sample), REML = F)
ann <- anova(fit_full$mer, fit_reduced$mer, fit_none$mer)
if (any(ann$`Pr(>Chisq)` <= 0.05, na.rm = T)) {
residuals <- predict(fit_full$gam, se.fit = T)$fit %>%
unname()
r.sq <- c(summary(fit_full$gam)$r.sq, summary(fit_reduced$gam)$r.sq) %>%
setNames(c("full", "reduced"))
out <- list(anova = ann,
residuals = residuals,
r.sq = r.sq)
return(out)
}
}, n.cores = 40, mc.preschedule = T, mc.cleanup = T, progress = F) %>%
.[!sapply(., is.null)]
qsave(res, "pseudotime_micro_l1.qs")res <- qread("pseudotime_micro_l1.qs")
rsq.pseudo <- res %>%
sapply(\(gene) gene$r.sq[1]) %>%
sort(decreasing = T)
res.filter <- res[names(rsq.pseudo)[seq(50)] %>% strsplit(".", fixed = T) %>% sget(1)]
cm.merged <- cao$data.object$getJointCountMatrix() %>%
.[match(names(pseudotime), rownames(.)), colnames(.) %in% names(res.filter)] %>%
.[rowSums(.) > 0, ]
## Predict smoothend expression
pseudotime.both <- pseudotime[rownames(cm.merged)]
weights.both <- sds_obj@assays@data@listData$weights %>% .[match(names(pseudotime.both), rownames(.)), 1] + 1E-7
scFit <- cm.merged %>%
Matrix::t() %>%
tradeSeq::fitGAM(pseudotime = pseudotime.both, cellWeights = weights.both, verbose = T)## Warning in max(abs(logR)): no non-missing arguments to max; returning -InfSmooth <- tradeSeq::predictSmooth(scFit, gene = colnames(cm.merged), tidy = F, n=100)
# Average across replicates and scale
Smooth <- t(scale(t(Smooth)))
# Seriate the results
Smooth <- Smooth[ seriation::get_order(seriation::seriate(Smooth, method="PCA_angle")), ]
# Create heatmap
col_fun = circlize::colorRamp2(c(-4, 0, 4), c("navy", "white", "firebrick"))
Heatmap(Smooth,
col = col_fun,
cluster_columns=F,
cluster_rows=F,
show_column_names = F,
row_names_gp = grid::gpar(fontsize = 5))
13
cpc <- pseudotime %>%
names() %>%
strsplit("_") %>%
sget(1) %>%
factor()
res.filter %>%
lget("residuals") %>%
lapply(as.data.frame) %>%
lapply(setNames, "residuals") %>%
lapply(mutate, group = cpc, pseudotime = pseudotime) %>%
data.table::rbindlist(idcol = "gene") %>%
ggplot(aes(pseudotime, residuals, col = group)) +
geom_smooth() +
theme_bw() +
facet_wrap(~gene, ncol = 5, scales = "free")## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'
Extended Data Figure 15
Here, we use public data from Feleke et al.. We isolated microglia based on CSF1R expression and performed a quick annotation using AIF1 expression to identify activated microglia.
con.micro <- qread("feleke_micro.qs")
anno.micro <- getConosCluster(con.micro) %>%
factor(labels = c("Cluster1", "Activated", "Cluster2", "Cluster2"))
# We set sample groups manually as it can't be inferred from sample names
sample.groups <- c("CTRL", "CTRL", "DLB", "DLB", "DLB", "DLB", "DLB", "PDD", "PDD", "PD", "PDD", "PD", "PDD", "PD", "PDD", "PDD", "DLB", "PD", "PDD", "PD", "DLB", "PD", "PD", "CTRL", "CTRL", "CTRL", "CTRL", "CTRL") %>%
setNames(names(con.micro$samples))con.clone <- con.micro$clone()
con.clone$samples <- con.micro$samples %>% .[which(sample.groups %in% c("CTRL", "PD"))]
sample.groups.clone <- sample.groups %>% .[. %in% c("CTRL", "PD")]
cao <- Cacoa$new(con.clone, sample.groups = sample.groups.clone, ref.level = "CTRL", target.level = "PD", cell.groups = anno.micro, n.cores = 32)
p <- cao$plotCellGroupSizes() + theme(line = element_blank())
p
cao$estimateCellLoadings()
p <- cao$plotCellLoadings()## Warning: Removed 651 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 651 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_text()`).
## Removed 1 row containing missing values or values outside the scale range
## (`geom_text()`).p
p <- con.clone$plotGraph(size = 0.3, groups = anno.micro, font.size = 5) + theme(line = element_blank())
p
p <- con.clone$plotGraph(gene = "AIF1", title = "AIF1", plot.na = F) + theme(line = element_blank())
p
## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 21546244 1150.7 39451381 2107.0 39451381 2107.0
## Vcells 5960924261 45478.3 11629033719 88722.5 11435508188 87246.1Extended Data Figure 16
16a
We provide the results output from scHLAcount in
scHLAcount.zip.
samples <- dir("scHLAcount/rydbirk/") %>% .[grepl(pattern = "_", .)]
cms <- lapply(samples, function(x) {
labels <- read.delim(paste0("scHLAcount/rydbirk/",x,"/labels.tsv"), header = F) %>% .[,1]
barcodes <- read.delim(paste0("scHLAcount/rydbirk/",x,"/barcodes.tsv"), header = F) %>%
.[,1] %>%
sapply(function(y) paste0(x,"one_",y))
mtx <- Matrix::readMM(paste0("scHLAcount/rydbirk/",x,"/count_matrix.mtx")) %>%
t() %>%
`dimnames<-`(list(barcodes, labels)) %>%
.[rowSums(.) > 0,] # Remove cells with no counts
tmp <- colnames(mtx) %>%
strsplit("*", T) %>%
sapply(`[[`, 1)
# If more than one allele per HLA type has been detected, sum per HLA type
if(any(tmp %>% table() %>% as.numeric() > 1)) {
cs <- colSums(mtx)
alleles <- unique(tmp)
mtx <- lapply(alleles, function(y) mtx[,tmp == y]) %>%
lapply(function(y) {
if(class(y) == "dgTMatrix") rowSums(y) else y
}) %>%
unlist() %>%
Matrix(nrow = nrow(mtx)) %>%
`rownames<-`(rownames(mtx))
}
colnames(mtx) <- unique(tmp)
return(mtx)
}) %>%
setNames(samples)Calculations
# Cell-wise
cell.counts <- sapply(cms, rowSums) %>%
unname() %>%
unlist() %>%
`names<-`(., gsub("one_","!!",names(.)))
depth <- getConosDepth(con) %>%
.[match(names(cell.counts), names(.))] %>%
{data.frame(cell = names(.),
sample = names(.) %>% strsplit("!!", T) %>% sapply(`[[`, 1),
depth = unname(.))}
depth[is.na(depth)] <- 0
cell.group <- grepl.replace(names(cell.counts), patterns = c("CTRL","PD","MSA")) %>% as.factor()
mhc1.raw <- sapply(cms, function(x) {
tmp <- x[,colnames(x) %in% c("A","B","C")]
if(class(tmp) != "numeric") rowSums(tmp) else tmp
}) %>%
unname() %>%
unlist() %>%
`names<-`(., gsub("one_","!!",names(.)))
mhc2.raw <- sapply(cms, function(x) {
tmp <- x[,!colnames(x) %in% c("A","B","C")]
if(class(tmp) != "numeric") rowSums(tmp) else tmp
}) %>%
unname() %>%
unlist() %>%
`names<-`(., gsub("one_","!!",names(.)))
cell.df <- data.frame(group = cell.group,
counts = cell.counts,
mhc1 = mhc1.raw,
mhc2 = mhc2.raw,
depth = depth$depth) %>%
mutate(., anno = anno.major[match(rownames(.), names(anno.major))],
sample = rownames(.) %>% sapply(strsplit, "!!", T) %>% sapply(`[[`, 1)) %>%
mutate(type = grepl.replace(anno %>% as.character(), levels(anno), c("Brain","Brain","Brain","Peripheral","Peripheral","Peripheral","Brain","Brain","Brain","Brain"))) %>%
`rownames<-`(names(cell.counts)) %>%
filter(!is.na(anno)) Load Smajic data
samples <- dir("scHLAcount/smajic") %>% .[grepl(pattern = "SRR", .)]
cms <- lapply(samples, function(x) {
labels <- read.delim(paste0("scHLAcount/smajic/",x,"/labels.tsv"), header = F) %>% .[,1]
barcodes <- read.delim(paste0("scHLAcount/smajic/",x,"/barcodes.tsv"), header = F) %>%
.[,1] %>%
sapply(function(y) paste0(x,"_",y,"_1"))
mtx <- Matrix::readMM(paste0("scHLAcount/smajic/",x,"/count_matrix.mtx")) %>%
t() %>%
`dimnames<-`(list(barcodes, labels)) %>%
.[rowSums(.) > 0,] # Remove cells with no counts
tmp <- colnames(mtx) %>%
strsplit("*", T) %>%
sapply(`[[`, 1)
# If more than one allele per HLA type has been detected, sum per HLA type
if(any(tmp %>% table() %>% as.numeric() > 1)) {
cs <- colSums(mtx)
alleles <- unique(tmp)
mtx <- lapply(alleles, function(y) mtx[,tmp == y]) %>%
lapply(function(y) {
if(class(y) == "dgTMatrix") rowSums(y) else y
}) %>%
unlist() %>%
Matrix(nrow = nrow(mtx)) %>%
`rownames<-`(rownames(mtx))
}
colnames(mtx) <- unique(tmp)
return(mtx)
}) %>%
setNames(samples)
# Correct naming
name.df <- data.frame(samples = samples, ids = c(rep("PD",6) %>% mapply(function(x,y) paste0(x,y), x = ., y = c(1,1,2:5)),
rep("CTRL",6) %>% mapply(function(x,y) paste0(x,y), x = ., y = 1:6)))
anno <- readRDS(paste0("scHLAcount/smajic/anno.sma.rds")) %>%
setNames(., names(.) %>%
strsplit("_", T) %>%
lapply(function(x) {
if(grepl("C", x[[1]])) x[[1]] %<>% gsub("C","CTRL", .)
return(x)
}) %>%
sapply(function(x) paste0(x[[1]],"_",x[[2]])))
cms <- 1:12 %>%
lapply(function(x) {
rnames <- cms[[x]] %>%
rownames() %>%
names() %>%
sapply(function(y) paste0(name.df[x,2],"_",y)) %>%
unname()
rownames(cms[[x]]) <- rnames
return(cms[[x]])
}) %>%
setNames(names(cms))Calculations
cell.df.rydbirk <- cell.df
# Cell-wise
cell.counts <- sapply(cms, rowSums) %>%
unname() %>%
unlist()
rem <- cell.counts %>%
names() %>%
table() %>%
.[. > 1] %>%
names()
cell.counts %<>% .[!names(.) %in% rem]
cell.group <- grepl.replace(names(cell.counts), patterns = c("CTRL","PD")) %>% as.factor()
mhc1.raw <- sapply(cms, function(x) {
tmp <- x[,colnames(x) %in% c("A","B","C")]
if(class(tmp) != "numeric") rowSums(tmp) else tmp
}) %>%
unname() %>%
unlist() %>%
.[!names(.) %in% rem]
mhc2.raw <- sapply(cms, function(x) {
tmp <- x[,!colnames(x) %in% c("A","B","C")]
if(class(tmp) != "numeric") rowSums(tmp) else tmp
}) %>%
unname() %>%
unlist() %>%
.[!names(.) %in% rem]
cell.df <- data.frame(group = cell.group,
counts = cell.counts,
mhc1 = mhc1.raw,
mhc2 = mhc2.raw) %>%
mutate(., anno = anno[match(rownames(.), names(anno))],
sample = rownames(.) %>% sapply(strsplit, "_", T) %>% sapply(`[[`, 1)) %>%
`rownames<-`(names(cell.counts)) %>%
filter(!is.na(anno))Integrate with our data and plot
cell.df.int <- rbind(cell.df.rydbirk %>%
select(-c("depth","type")) %>%
mutate(origin = "Rydbirk"),
cell.df %>%
mutate(anno = anno %>% factor(labels = c("Astrocytes","Exc. neurons","Inh. neurons","Endothelial","Eppendymal","Exc. neurons","Inh. neurons","Inh. neurons","Microglia","Oligodendrocytes","OPCs","Pericytes")),
origin = "Smajic")) %>%
filter(! anno %in% c("Blood_immune","Endothelial","Eppendymal","Mural","Pericytes","Pericytes/endothelial","Immune")) %>%
filter(!is.na(anno)) %>%
mutate(anno = anno %>% factor(),
origin = origin %>% factor(),
anno = anno %>% unname() %>% factor(),
sample = sample %>% unname())
cell.anno.df <-
cell.df.int %>%
group_by(anno, sample, group, origin) %>%
summarise(mhc1 = mean(mhc1),
mhc2 = mean(mhc2),
counts = mean(counts),
cells = length(sample)) %>%
as.data.frame() %>%
select(-cells, -counts, -sample)## `summarise()` has regrouped the output.
## ℹ Summaries were computed grouped by anno, sample, group, and origin.
## ℹ Output is grouped by anno, sample, and group.
## ℹ Use `summarise(.groups = "drop_last")` to silence this message.
## ℹ Use `summarise(.by = c(anno, sample, group, origin))` for per-operation
## grouping (`?dplyr::dplyr_by`) instead.p.text1 <- data.frame(signif = c("n.s.", "n.s.", "n.s.", "n.s.", "n.s.", "0.040", "0.032", "n.s."),
mhc1 = 4.5,
anno = levels(cell.anno.df$anno))
p.text2 <- data.frame(signif = c("0.00057"),
mhc2 = 8.5,
anno = " Microglia")
cowplot::plot_grid(plotlist = list(
ggplot(cell.anno.df,
aes(anno,
mhc1)) +
geom_boxplot(outlier.shape = NA,
aes(fill = group)) +
geom_point(position = position_jitterdodge(jitter.width = 0.2),
aes(fill = group,
col = origin,
shape = group),
size = 2) +
scale_color_manual(values = c("black",
"grey50")) +
labs(x = "",
y = "Mean counts per sample",
title = "MHC-I counts",
fill = "",
col = "",
shape = " ") +
geom_text(data = p.text1, aes(label = signif)) +
theme_bw() +
scale_fill_manual(values = pal.major) +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
line = element_blank()),
ggplot(cell.anno.df %>% filter(anno == "Microglia") %>% mutate(anno = " Microglia"), aes(anno, mhc2)) +
geom_boxplot(outlier.shape = NA, aes(fill = group)) +
geom_point(position = position_jitterdodge(jitter.width = 0.2), aes(fill = group, col = origin, shape = group), size = 2) +
scale_color_manual(values = c("black","grey50")) +
labs(x = "", y = "", title = "MHC-II counts", fill = "", col = "", shape = " ") +
geom_text(data = p.text2, aes(label = signif)) +
theme_bw() +
scale_fill_manual(values = pal.major) +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
line = element_blank())
), ncol = 2, rel_widths = c(2,0.9))
Kruskal-Wallis, asses differences per cell type
cell.anno.df %>%
group_by(anno) %>%
kruskal_test(mhc1 ~ group) %>%
data.frame()## anno .y. n statistic df p method
## 1 Astrocytes mhc1 38 5.4099627 2 0.0669 Kruskal-Wallis
## 2 Microglia mhc1 37 3.5900071 2 0.1660 Kruskal-Wallis
## 3 Oligodendrocytes mhc1 38 4.0883941 2 0.1290 Kruskal-Wallis
## 4 PVMs mhc1 24 3.5403947 2 0.1700 Kruskal-Wallis
## 5 OPCs mhc1 38 0.9689609 2 0.6160 Kruskal-Wallis
## 6 Inh. neurons mhc1 31 6.4335165 2 0.0401 Kruskal-Wallis
## 7 Exc. neurons mhc1 22 6.8860651 2 0.0320 Kruskal-Wallis
## 8 MSN mhc1 21 4.9345432 2 0.0848 Kruskal-Walliscell.anno.df %>%
filter(anno == "Microglia") %>%
group_by(anno) %>%
kruskal_test(mhc2 ~ group) %>%
data.frame()## anno .y. n statistic df p method
## 1 Microglia mhc2 37 14.92493 2 0.000574 Kruskal-WallisWilcoxon, assess differences between our data and Smajic per condition per cell type
cell.anno.df %>%
filter(group != "MSA",
!anno %in% c("MSN","PVMs")) %>%
group_by(anno, group) %>%
wilcox_test(mhc1 ~ origin) %>%
data.frame()## anno group .y. group1 group2 n1 n2 statistic p
## 1 Astrocytes CTRL mhc1 Rydbirk Smajic 10 6 27 0.792
## 2 Astrocytes PD mhc1 Rydbirk Smajic 11 5 26 0.913
## 3 Microglia CTRL mhc1 Rydbirk Smajic 9 6 21 0.529
## 4 Microglia PD mhc1 Rydbirk Smajic 11 5 30 0.827
## 5 Oligodendrocytes CTRL mhc1 Rydbirk Smajic 10 6 34 0.713
## 6 Oligodendrocytes PD mhc1 Rydbirk Smajic 11 5 26 0.913
## 7 OPCs CTRL mhc1 Rydbirk Smajic 10 6 15 0.118
## 8 OPCs PD mhc1 Rydbirk Smajic 11 5 31 0.743
## 9 Inh. neurons CTRL mhc1 Rydbirk Smajic 8 6 10 0.080
## 10 Inh. neurons PD mhc1 Rydbirk Smajic 7 5 23 0.432
## 11 Exc. neurons CTRL mhc1 Rydbirk Smajic 6 6 10 0.240
## 12 Exc. neurons PD mhc1 Rydbirk Smajic 3 5 5 0.549cell.anno.df %>%
filter(group != "MSA",
anno == "Microglia") %>%
group_by(anno, group) %>%
wilcox_test(mhc2 ~ origin) %>%
data.frame()## anno group .y. group1 group2 n1 n2 statistic p
## 1 Microglia CTRL mhc2 Rydbirk Smajic 9 6 19 0.368
## 2 Microglia PD mhc2 Rydbirk Smajic 11 5 40 0.18016b
We provide a curated list with MHC and cytokine genes. Please note, the order of clusters may change.
dat <- read.table("MHC_cytokines_curated.tsv", header = T, sep = "\t")
cm.merged <- con$getJointCountMatrix(raw = T) %>%
Matrix::t() %>%
.[, colnames(.) %in% names(anno.major)] %>%
.[rownames(.) %in% (dat$name[dat$class == "cytokine"] %>% gsub("-","",.)), ] %>%
.[rowSums(.) > 0,]
# Create sample-wise annotation
anno.donor <- con$getDatasetPerCell()[colnames(cm.merged)]
anno.subtype <- anno.major %>%
.[!is.na(.) & (. %in% c("Microglia", "PVMs"))] %>%
factor()
idx <- intersect(anno.donor %>% names(), anno.subtype %>% names())
anno.donor %<>% .[idx]
anno.subtype %<>% .[idx] %>%
{`names<-`(as.character(.), names(.))}
anno.final <- paste0(anno.donor,"!!",anno.subtype) %>%
`names<-`(anno.donor %>% names())
# Create pseudo CM
cm.pseudo <- sccore::collapseCellsByType(cm.merged %>% Matrix::t(),
groups = anno.final, min.cell.count = 1) %>%
t() %>%
apply(2, as, "integer")
cm.pseudo %<>%
{. + 1} %>%
DESeq2::DESeqDataSetFromMatrix(.,
colnames(.) %>%
strsplit("_") %>%
sget(1) %>%
data.frame() %>%
`dimnames<-`(list(colnames(cm.pseudo), "group")),
design = ~ group) %>%
DESeq2::estimateSizeFactors() %>%
DESeq2::counts(normalized = T)## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factorscm.pseudo %<>%
Matrix::t() %>%
scale() %>%
Matrix::t()
ord <- cm.pseudo %>%
colnames() %>%
{data.frame(id = .)} %>%
mutate(.,
ct = strsplit(id, "!!") %>%
sget(2),
condition = strsplit(id, "!!|_") %>%
sget(1)) %>%
arrange(ct, condition) %>%
pull(id)
cm.pseudo %<>%
.[, match(ord, colnames(.))]
cm.pseudo %<>%
as.data.frame() %>%
tibble::rownames_to_column(var = "gene") %>%
reshape2::melt(id.vars = c("gene")) %>%
mutate(variable = as.character(variable),
condition = strsplit(variable, "!!|_") %>%
sget(1),
ct = strsplit(variable, "!!") %>%
sget(2)) %>%
group_by(condition, ct, gene) %>%
summarize(m = mean(value)) %>%
ungroup() %>%
mutate(variable = paste(condition, ct, sep = "!!")) %>%
select(-condition, -ct) %>%
reshape2::dcast(gene ~ variable, value.var = "m") %>%
tibble::column_to_rownames(var = "gene")## `summarise()` has regrouped the output.
## ℹ Summaries were computed grouped by condition, ct, and gene.
## ℹ Output is grouped by condition and ct.
## ℹ Use `summarise(.groups = "drop_last")` to silence this message.
## ℹ Use `summarise(.by = c(condition, ct, gene))` for per-operation grouping
## (`?dplyr::dplyr_by`) instead.tmp <- cm.pseudo %>%
colnames() %>%
strsplit("_|!!")
clusters <- tmp %>%
sget(2)
condition <- tmp %>%
sget(1)
ha <- ComplexHeatmap::HeatmapAnnotation(Annotation = clusters,
Condition = condition,
col = list(Annotation = c("Microglia" = unname(pal.major["Microglia"]),
"PVMs" = unname(pal.major["PVMs"])),
Condition = c("CTRL" = unname(pal.major["CTRL"]),
"MSA" = unname(pal.major["MSA"]),
"PD" = unname(pal.major["PD"]))
))
ComplexHeatmap::Heatmap(cm.pseudo,
name = "Expression",
show_column_names = F,
show_row_names = T,
cluster_columns = F,
show_column_dend = F,
cluster_rows = T,
top_annotation = ha,
show_row_dend = F,
column_split = colnames(cm.pseudo) %>% strsplit("!!|_") %>% sget(2) %>% unname() %>% factor(),
col=circlize::colorRamp2(c(-1, 1.2), c("white", "firebrick")),
row_km = 4)## Warning: The input is a data frame-like object, convert it to a matrix.
Extended Data Figure 17
17c
c("FOSL2", "TCF4", "STAT1", "NR3C1", "ETV7", "THRB", "BPTF", "RELB", "CEBPD", "HIF1A", "ELF1", "CREM", "ELF2", "ZNF44", "CHD1", "POU2F1", "GTF2IRD1", "NFIA", "NFE2L2", "FOXP1", "FOXO3") %>%
clusterProfiler::enrichGO("org.Hs.eg.db", "SYMBOL", "BP") %>%
enrichplot::pairwise_termsim() %>%
enrichplot::treeplot() +
scale_fill_manual(values = brewer.pal(6, "Greys")[-1])## ## Warning: `aes_()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`
## ℹ The deprecated feature was likely used in the ggtree package.
## Please report the issue at <https://github.com/YuLab-SMU/ggtree/issues>.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning in fortify(data, ...): Arguments in `...` must be used.
## ✖ Problematic arguments:
## • as.Date = as.Date
## • yscale_mapping = yscale_mapping
## • show.legend = FALSE
## ℹ Did you misspell an argument name?## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
Extended Data Figure 18
18a
We provide the results from scDRS based on Nalls et al. GWAS
summary stats. For calculation of these, see
scDRS_PD.ipynb.
dat.scores <- qread("gwas/nalls/PD.full_score.qs")
# Load downstream analyses for cell types versus trait
dat.ct <- read.delim("gwas/nalls/downstream_celltype.tsv", header = F, sep = "\t") %$%
strsplit(V1, " ") %>%
unlist() %>%
gsub("\\", "", ., fixed = T) %>%
gsub("\n", "", ., fixed = T) %>%
.[!sapply(., \(x) x == "")] %>%
.[c(1:5,69:72, # Header
8:12,75:78, # Astro
15:19,81:84, # EN
21:25,86:89, # Immune
28:32,92:95, # IN
34:38,97:100, # MSN
40:44,102:105, # MIC
46:50,107:110, # OPCs
52:56,112:115, # OL
58:62,117:120, # PVMs
64:68,122:125 # Peri
)]
dat.sign <- dat.ct %>%
split(seq(9)) %>%
bind_rows() %>%
{`colnames<-`(.[-1, ], .[1, ])} %>%
as.data.frame()
dat.sign %<>% mutate(group = c("Astrocytes",
"Exc. neurons",
"Immune",
"Inh. neurons",
"MSN",
"Microglia",
"OPCs",
"Oligodendrocytes",
"PVMs",
"Pericytes/endothelial"))
dat.sign %<>%
filter(!group == "Unknown") %>%
mutate(assoc_sign = as.numeric(assoc_mcp) %>% sapply(\(x) if (x <= 0.05) paste("*", formatC(x, digits = 2), sep = " ") else ""),
hetero_sign = as.numeric(hetero_mcp) %>% sapply(\(x) if (x <= 0.05) paste("#", formatC(x, digits = 2), sep = " ") else " ")) %>%
mutate(sign = paste0(assoc_sign,"\n",hetero_sign) %>% gsub("\n ", "", .)) %>%
dplyr::rename(type = group)
p <- data.frame(cell = dat.scores$X,
score = dat.scores$norm_score,
type = anno.major[match(dat.scores$X,
names(anno.major))]) %>%
group_by(type) %>%
summarize(m = median(score)) %>%
filter(!is.na(type)) %>%
mutate(type = as.character(type)) %$%
arrange(., desc(m)) %>%
mutate(type = factor(type, levels = type), sign = dat.sign$sign[match(type %>% levels, dat.sign$type)]) %>%
ggplot(aes(type, m, fill = type)) +
geom_bar(stat = "identity") +
geom_text(aes(label = sign), vjust = -0.5) +
theme_bw() +
labs(y = "Mean score", x = "", title = "scDRS mean scores and associations") +
guides(fill = "none") +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
line = element_blank()) +
ylim(c(-0.6, 2.6)) +
scale_fill_manual(values = pal.major) +
geom_hline(yintercept = 0)
p
18b
dat.ct <- read.delim("gwas/nalls/downstream_celltype_microglia.tsv", header = F, sep = "\t") %$%
strsplit(V1, " ") %>%
unlist() %>%
gsub("\\", "", ., fixed = T) %>%
gsub("\n", "", ., fixed = T) %>%
gsub("group", "", .) %>%
.[!sapply(., \(x) x == "")] %>%
{c("group", .[seq(35)])} %>%
matrix(ncol = 6, byrow = T) %>%
as.data.frame() %>%
`colnames<-`(., .[1, ]) %>%
.[-1, ]
anno.micro <- qread("anno_micro_pvm.qs") %>%
renameAnnotation("Steady-state","MIC_steady-state") %>%
renameAnnotation("Intermediate1","MIC_intermediate1") %>%
renameAnnotation("Intermediate2","MIC_intermediate2") %>%
renameAnnotation("Activated","MIC_activated") %>%
.[!. == "PVMs"] %>%
factor()
dat.sign <-
dat.ct %>%
filter(!group == "Unknown") %>%
mutate(assoc_sign = as.numeric(assoc_mcp) %>% sapply(\(x) if (x <= 0.05) paste("*", formatC(x, digits = 2), sep = " ") else ""),
hetero_sign = as.numeric(hetero_mcp) %>% sapply(\(x) if (x <= 0.05) paste("#", formatC(x, digits = 2), sep = " ") else " ")) %>%
mutate(sign = paste0(assoc_sign,"\n",hetero_sign) %>% gsub("\n ", "", .)) %>%
dplyr::rename(type = group)
p <- data.frame(cell = dat.scores$X %>% gsub("one_", "!!", .),
score = dat.scores$norm_score,
type = anno.major[match(dat.scores$X,
names(anno.major))]) %>%
filter(cell %in% names(anno.micro)) %>%
mutate(type = factor(anno.micro[.$cell])) %>%
group_by(type) %>%
summarize(m = mean(score)) %>%
filter(!is.na(type)) %>%
mutate(type = as.character(type)) %>%
arrange(desc(m)) %>%
mutate(type = factor(type, levels = type), sign = dat.sign$sign[match(type %>% levels, dat.sign$type)]) %>%
mutate(type = factor(type, levels = type)) %>%
ggplot(aes(type, m, fill = type)) +
geom_bar(stat = "identity") +
geom_text(aes(label = sign), vjust = -0.5, nudge_y = -0.05) +
theme_bw() +
theme(line = element_blank()) +
labs(y = "Mean score", x = "", title = "scDRS for microglia") +
guides(fill = "none") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
ylim(c(0, 2)) +
scale_fill_manual(values = pal.major)
p
18c
We provide the results from scDRS based on Chia et al. GWAS
summary stats. For calculation of these, see
scDRS_MSA.ipynb.
dat.scores <- qread("gwas/chia/MSA.full_score.qs")
# Load downstream analyses for cell types versus trait
dat.ct <- read.delim("gwas/chia/downstream_celltype.tsv", header = F, sep = "\t") %$%
strsplit(V1, " ") %>%
unlist() %>%
gsub("\\", "", ., fixed = T) %>%
gsub("\n", "", ., fixed = T) %>%
.[!sapply(., \(x) x == "")] %>%
.[c(1:5,69:72, # Header
8:12,75:78, # Astro
15:19,81:84, # EN
21:25,86:89, # Immune
28:32,92:95, # IN
34:38,97:100, # MSN
40:44,102:105, # MIC
46:50,107:110, # OPCs
52:56,112:115, # OL
58:62,117:120, # PVMs
64:68,122:125 # Peri
)]
dat.sign <- dat.ct %>%
split(seq(9)) %>%
bind_rows() %>%
{`colnames<-`(.[-1, ], .[1, ])} %>%
as.data.frame()
dat.sign %<>% mutate(group = c("Astrocytes",
"Exc. neurons",
"Immune",
"Inh. neurons",
"MSN",
"Microglia",
"OPCs",
"Oligodendrocytes",
"PVMs",
"Pericytes/endothelial"))
dat.sign %<>%
filter(!group == "Unknown") %>%
mutate(assoc_sign = as.numeric(assoc_mcp) %>% sapply(\(x) if (x <= 0.05) paste("*", formatC(x, digits = 2), sep = " ") else ""),
hetero_sign = as.numeric(hetero_mcp) %>% sapply(\(x) if (x <= 0.05) paste("#", formatC(x, digits = 2), sep = " ") else " ")) %>%
mutate(sign = paste0(assoc_sign,"\n",hetero_sign) %>% gsub("\n ", "", .)) %>%
dplyr::rename(type = group)
p <- data.frame(cell = dat.scores$X,
score = dat.scores$norm_score,
type = anno.major[match(dat.scores$X,
names(anno.major))]) %>%
group_by(type) %>%
summarize(m = median(score)) %>%
filter(!is.na(type)) %>%
mutate(type = as.character(type)) %$%
arrange(., desc(m)) %>%
mutate(type = factor(type, levels = type), sign = dat.sign$sign[match(type %>% levels, dat.sign$type)]) %>%
ggplot(aes(type, m, fill = type)) +
geom_bar(stat = "identity") +
geom_text(aes(label = sign), vjust = .5) +
theme_bw() +
labs(y = "Mean score", x = "", title = "scDRS mean scores and associations") +
guides(fill = "none") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
line = element_blank()) +
ylim(c(-0.5, 0.5)) +
scale_fill_manual(values = pal.major) +
geom_hline(yintercept = 0)
p
Extended Data Figure 19
All subfigures were made in GraphPad Prism. Data are available in
Phagocytosis_assay_triplicates_raw_data_HH.tsv (Copenhagen
experiment) or HOUSTON.tsv (Houston experiment).
Extended Data Table 2
Not rerun
anno.major <- qread("anno_major.qs")
anno.micro <- qread("anno_micro_pvm.qs") %>%
renameAnnotation("Steady-state","MIC_steady-state") %>%
renameAnnotation("Intermediate1","MIC_intermediate1") %>%
renameAnnotation("Intermediate2","MIC_intermediate2") %>%
renameAnnotation("Activated","MIC_activated")
anno.glia <- c("anno_astro.qs",
"anno_oligo.qs",
"anno_opc.qs") %>%
lapply(qread) %>%
Reduce(c, .) %>%
factor() %>%
renameAnnotation("Homeostatic_astrocytes", "AS_homeostatic") %>%
renameAnnotation("Reactive_astrocytes", "AS_reactive") %>%
renameAnnotation("Homeostatic_LINC01608", "OL_LINC01608") %>%
renameAnnotation("Homeostatic_SLC5A11", "OL_SLC5A11") %>%
renameAnnotation("Reactive_SCGZ", "OL_SGCZ")
anno.neurons <- qread("anno_neurons.qs")
# Minor final
anno.minor <- anno.major[!anno.major %in% c("Inh. neurons", "Exc. neurons", "PVMs", "Astrocytes", "Oligodendrocytes", "OPCs", "Microglia")] %>%
{factor(c(., anno.neurons, anno.glia, anno.micro))} %>%
factor(levels = sort(levels(.)))
# Major final
anno.major %<>% .[!names(.) %in% names(anno.neurons)] %>%
{factor(c(., anno.neurons))} %>%
collapseAnnotation("MSN") %>%
collapseAnnotation("GABAergic") %>%
collapseAnnotation("GLUergic") %>%
renameAnnotation("MSN", "Medium spiny neurons") %>%
renameAnnotation("GABAergic", "GABAergic neurons") %>%
renameAnnotation("GLUergic", "GLUergic neurons") %>%
factor(levels = sort(levels(.)))con.major$n.cores <- 32 # Major object
markers.major <- con.major$getDifferentialGenes(groups = anno.major,
z.threshold = 1,
upregulated.only = T,
append.specificity.metrics = T,
append.auc = T)
markers.major %>%
lapply(filter, PAdj <= 0.05) %>%
bind_rows(.id = "Celltype") %>%
write.table("Table SX - Celltype markers, major annotation.tsv", sep = "\t", dec = ".", row.names = F)Extended Data Table 4
Not rerun
res <- list(
Major = list(CTRLvsMSA = "cao_major_msa.qs",
CTRLvsPD = "cao_major_pd.qs",
PDvsMSA = "cao_major_dis.qs"),
Neurons = list(CTRLvsMSA = "cao_neurons_msa.qs",
CTRLvsPD = "cao_neurons_pd.qs",
PDvsMSA = "cao_neurons_dis.qs"),
Glial = list(CTRLvsMSA = "cao_oligo_astro_opc_msa.qs",
CTRLvsPD = "cao_oligo_astro_opc_pd.qs",
PDvsMSA = "cao_oligo_astro_opc_dis.qs"),
Micro_PVM = list(CTRLvsMSA = "cao_micro_pvm_msa.qs",
CTRLvsPD = "cao_micro_pvm_pd.qs",
PDvsMSA = "cao_micro_pvm_dis.qs")) %>%
lapply(\(celltype) sccore::plapply(celltype, \(comp) qread(comp, nthreads = 10)$test.results$coda, n.cores = 3)) %>%
lapply(lapply, \(x) data.frame(subtype = names(x$padj),
loadings.min = apply(x$loadings, 1, min),
loadings.median = apply(x$loadings, 1, median),
loadings.max = apply(x$loadings, 1, max),
padj = unname(x$padj))) %>%
lapply(bind_rows, .id = "Comparison") %>%
bind_rows(.id = "Cell type") %>%
`rownames<-`(NULL)
res %>%
write.table("Table SX - Loadings.tsv", sep = "\t", dec = ".", row.names = F)Extended Data Table 5
Not rerun
cao <- Cacoa$new(data.object = con, # Major object
cell.groups = anno.major,
ref.level = "CTRL",
target.level = "DISEASE",
sample.groups = con$samples %>%
names() %>%
strsplit("_") %>%
sget(1) %>%
{ifelse(. == "CTRL", "CTRL", "DISEASE")} %>%
`names<-`(con$samples %>%
names()),
sample.groups.palette = c(c("yellow", RColorBrewer::brewer.pal(n = 6, "Accent")[5:6]) %>% setNames(c("CTRL","PD","MSA"))))
cao$sample.groups <- con$samples %>%
names() %>%
strsplit("_") %>%
sget(1) %>%
`names<-`(con$samples %>%
names())
res <- cao$plotCellGroupSizes(show.significance = TRUE,
filter.empty.cell.types = FALSE)$data %>%
group_by(group, variable) %>%
summarize(Min_proportion = min(value),
Mean_proportion = mean(value),
Max_proportion = max(value)) %>%
dplyr::rename(Condition = group,
Celltype = variable)
res %>%
write.table("Table SX - Proportions, major annotation.tsv", sep = "\t", dec = ".", row.names = F)Extended Data Table 6
Not rerun
anno.major <- qread("anno_major.qs")
anno.micro <- qread("anno_micro_pvm.qs") %>%
renameAnnotation("Steady-state","MIC_steady-state") %>%
renameAnnotation("Intermediate1","MIC_intermediate1") %>%
renameAnnotation("Intermediate2","MIC_intermediate2") %>%
renameAnnotation("Activated","MIC_activated")
anno.glia <- c("anno_astro.qs",
"anno_oligo.qs",
"anno_opc.qs") %>%
lapply(qread) %>%
Reduce(c, .) %>%
factor() %>%
renameAnnotation("Homeostatic_astrocytes", "AS_homeostatic") %>%
renameAnnotation("Reactive_astrocytes", "AS_reactive") %>%
renameAnnotation("Homeostatic_LINC01608", "OL_LINC01608") %>%
renameAnnotation("Homeostatic_SLC5A11", "OL_SLC5A11") %>%
renameAnnotation("Reactive_SCGZ", "OL_SGCZ")
anno.neurons <- qread("anno_neurons.qs")
# Minor final
anno.minor <- anno.major[!anno.major %in% c("Inh. neurons", "Exc. neurons", "PVMs", "Astrocytes", "Oligodendrocytes", "OPCs", "Microglia")] %>%
{factor(c(., anno.neurons, anno.glia, anno.micro))} %>%
factor(levels = sort(levels(.)))
# Major final
anno.major %<>% .[!names(.) %in% names(anno.neurons)] %>%
{factor(c(., anno.neurons))} %>%
collapseAnnotation("MSN") %>%
collapseAnnotation("GABAergic") %>%
collapseAnnotation("GLUergic") %>%
renameAnnotation("MSN", "Medium spiny neurons") %>%
renameAnnotation("GABAergic", "GABAergic neurons") %>%
renameAnnotation("GLUergic", "GLUergic neurons") %>%
factor(levels = sort(levels(.)))markers.minor <- con.major$getDifferentialGenes(groups = anno.minor,
z.threshold = 1,
upregulated.only = T,
append.specificity.metrics = T,
append.auc = T)
markers.minor %>%
lapply(filter, PAdj <= 0.05) %>%
bind_rows(.id = "Celltype") %>%
write.table("Table SX - Celltype markers, minor annotation.tsv", sep = "\t", dec = ".", row.names = F)Extended Data Table 7
Not rerun
cao <- Cacoa$new(data.object = con,
cell.groups = anno.minor,
ref.level = "CTRL",
target.level = "DISEASE",
sample.groups = con$samples %>%
names() %>%
strsplit("_") %>%
sget(1) %>%
{ifelse(. == "CTRL", "CTRL", "DISEASE")} %>%
`names<-`(con$samples %>%
names()),
sample.groups.palette = c(c("yellow", RColorBrewer::brewer.pal(n = 6, "Accent")[5:6]) %>% setNames(c("CTRL","PD","MSA"))))
cao$sample.groups <- con$samples %>%
names() %>%
strsplit("_") %>%
sget(1) %>%
`names<-`(con$samples %>%
names())
res <- cao$plotCellGroupSizes(show.significance = TRUE,
filter.empty.cell.types = FALSE)$data %>%
group_by(group, variable) %>%
summarize(Min_proportion = min(value),
Mean_proportion = mean(value),
Max_proportion = max(value)) %>%
dplyr::rename(Condition = group,
Celltype = variable)
res %>%
write.table("Table SX - Proportions, minor annotation.tsv", sep = "\t", dec = ".", row.names = F)Extended Data Table 8
Not rerun
First, correct OPCs/COPs for age covariate according to scITD calculations
cao_msa <- qread("cao_opc_msa.qs", nthreads = 10)
cao_msa$plot.theme <- theme_bw()
cao_pd <- qread("cao_opc_pd.qs", nthreads = 10)
cao_pd$plot.theme <- theme_bw()
cao_dis <- qread("cao_opc_dis.qs", nthreads = 10)
cao_dis$plot.theme <- theme_bw()
metadata.all <- read.delim("metadata.tsv")
meta.df.msa <- metadata.all %>%
filter(sample %in% names(cao_msa$data.object$samples)) %>%
tibble::column_to_rownames("sample") %>%
select(age)
meta.df.pd <- metadata.all %>%
filter(sample %in% names(cao_pd$data.object$samples)) %>%
tibble::column_to_rownames("sample") %>%
select(age)
meta.df.dis <- metadata.all %>%
filter(sample %in% names(cao_dis$data.object$samples)) %>%
tibble::column_to_rownames("sample") %>%
select(age)
all.genes_msa <- cao_msa$data.object$samples %>%
lget("misc") %>%
lget("rawCounts") %>%
lapply(colnames) %>%
Reduce(union, .)
genes.to.omit_msa <- all.genes_msa %>%
.[grepl("MT-|RPL|RPS", .)]
all.genes_pd <- cao_pd$data.object$samples %>%
lget("misc") %>%
lget("rawCounts") %>%
lapply(colnames) %>%
Reduce(union, .)
genes.to.omit_pd <- all.genes_pd %>%
.[grepl("MT-|RPL|RPS", .)]
all.genes_dis <- cao_dis$data.object$samples %>%
lget("misc") %>%
lget("rawCounts") %>%
lapply(colnames) %>%
Reduce(union, .)
genes.to.omit_dis <- all.genes_dis %>%
.[grepl("MT-|RPL|RPS", .)]
cao_msa$estimateDEPerCellType(covariates = meta.df.msa, name = "de_age", genes.to.omit = genes.to.omit_msa)
cao_pd$estimateDEPerCellType(covariates = meta.df.pd, name = "de_age", genes.to.omit = genes.to.omit_pd)
cao_dis$estimateDEPerCellType(covariates = meta.df.dis, name = "de_age", genes.to.omit = genes.to.omit_dis)
cao_msa$estimateOntology("GSEA", name = "gsea_age", de.name = "de_age", org.db = org.Hs.eg.db::org.Hs.eg.db)
cao_pd$estimateOntology("GSEA", name = "gsea_age", de.name = "de_age", org.db = org.Hs.eg.db::org.Hs.eg.db)
cao_dis$estimateOntology("GSEA", name = "gsea_age", de.name = "de_age", org.db = org.Hs.eg.db::org.Hs.eg.db)
qsave(cao_msa, "cao_opc_msa.qs", nthreads = 10)
qsave(cao_pd, "cao_opc_pd.qs", nthreads = 10)
qsave(cao_dis, "cao_opc_dis.qs", nthreads = 10)go.list <- dir("cacoa/", full.names = T)[-c(4:7, 15:18, 22, 26)] %>%
lapply(qread, nthreads = 10) %>%
lapply(purrr::pluck, "test.results") %>%
lapply(\(x) if ("gsea_age" %in% names(x)) x$gsea_age else x$gsea) %>% # To include age-corrected calculations for OPCs
setNames(dir("cacoa/")[-c(4:7, 15:18, 22, 26)])
go.list %>%
lget("res") %>%
lapply(lapply, lapply, slot, "result") %>%
lapply(lapply, data.table::rbindlist, idcol = "Category") %>%
lapply(data.table::rbindlist, idcol = "Celltype") %>%
data.table::rbindlist(idcol = "Comparison") %>%
mutate(Comparison = sapply(Comparison, \(comp) {
if (grepl("dis", comp)) "PD vs MSA" else if (grepl("msa", comp)) "CTRL vs MSA" else "CTRL vs PD"
})) %>%
filter(p.adjust <= 0.05) %>%
write.table("GSEA.csv", sep = ",", dec = ".", row.names = F)Extended Data Table 9
Not rerun
de.list <- dir("cacoa/", full.names = T)[-c(4:7, 15:18, 22, 26)] %>%
lapply(qread, nthreads = 10) %>%
lapply(purrr::pluck, "test.results") %>%
lapply(\(x) if ("de_age" %in% names(x)) x$de_age else x$de) %>% # To include results from age-corrected calculations for OPCs
setNames(dir("cacoa/")[-c(4:7, 15:18, 22, 26)])
de.list %>%
lapply(lget, "res") %>%
lapply(data.table::rbindlist, idcol = "Celltype") %>%
lapply(select, Celltype, baseMean, log2FoldChange, lfcSE, stat, pvalue, padj, Gene, Z, Za, CellFrac, SampleFrac) %>%
data.table::rbindlist(idcol = "Comparison") %>%
mutate(Comparison = sapply(Comparison, \(comp) {
if (grepl("dis", comp)) "PD vs MSA" else if (grepl("msa", comp)) "CTRL vs MSA" else "CTRL vs PD"
})) %>%
filter(padj <= 0.05) %>%
write.table("DEGs.csv", sep = ",", dec = ".", row.names = F)Extended Data Table 10
Not rerun. We need the res.filter object from calculation of smoothed heatmap for microglia activation trajectory.
go <- clusterProfiler::enrichGO(names(res.filter),
OrgDb = "org.Hs.eg.db",
keyType = "SYMBOL",
universe = colnames(con$getJointCountMatrix()))
go@result %>%
filter(pvalue <= 0.05) %>%
write.table("Microglia_pseudotime_activation-lineage_GO.tsv", sep = "\t", row.names = F)Extended Data Table 11
Not rerun. We need the Smooth object for the microglia to PVM trajectory.
go.list <- list(repressed = rownames(Smooth)[1:which(rownames(Smooth) == "EPB41L2")],
induced = rownames(Smooth)[(which(rownames(Smooth) == "EPB41L2")+1):nrow(Smooth)]) %>%
lapply(clusterProfiler::enrichGO,
OrgDb = "org.Hs.eg.db",
keyType = "SYMBOL",
universe = colnames(con$getJointCountMatrix()))
go.list %>%
lapply(\(x) x@result) %>%
lapply(filter, pvalue <= 0.05) %>%
data.table::rbindlist(idcol = "geneset") %>%
write.table("Microglia_pseudotime_PVM-lineage_GO.tsv", sep = "\t", row.names = F)Extended Data Table 14
Not rerun. We use the publicly available data from Rydbirk et al..
# Create universes
dat.all <- read.delim("CSF-Sv_Soluble-frac__Report.tsv")
universe.gene <- dat.all %>%
pull(PG.Genes) %>%
unique()
universe.prot <- dat.all %>%
pull(PG.UniProtIds)
# DEPs
dat <- read.delim("Soluble_fraction.txt")
# KEGG, MSA
kegg.res <- dat %>%
filter(Disease.group == "MSA") %>%
pull(UniProt.ID) %>%
clusterProfiler::enrichKEGG(keyType = "uniprot", pvalueCutoff = 0.2, universe = universe.prot, organism = "hsa")
# KEGG, PD
kegg.res.pd <- dat %>%
filter(Disease.group == "PD") %>%
pull(UniProt.ID) %>%
clusterProfiler::enrichKEGG(keyType = "uniprot", pvalueCutoff = 0.2, universe = universe.prot, organism = "hsa")
# Write table
list(MSA = kegg.res, PD = kegg.res.pd) %>%
lapply(getElement, "result") %>%
lapply(dplyr::select, Description, GeneRatio, BgRatio, pvalue, p.adjust, ID, geneID) %>%
bind_rows(.id = "Disease") %>%
write.table("TableS14_KEGG.tsv", sep = "\t", row.names = F)Session info
Time to knit
Sys.time() - tt## Time difference of 26.09581 minssessionInfo()## R version 4.5.2 (2025-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.3 LTS
##
## Matrix products: default
## BLAS/LAPACK: /opt/intel/oneapi/mkl/2025.3/lib/libmkl_gf_lp64.so.2; LAPACK version 3.12.1
##
## Random number generation:
## RNG: L'Ecuyer-CMRG
## Normal: Inversion
## Sample: Rejection
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=C
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Europe/Berlin
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 grid stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] ggraph_2.2.2 scITD_1.0.4
## [3] gamm4_0.2-7 mgcv_1.9-4
## [5] nlme_3.1-168 lme4_1.1-38
## [7] slingshot_2.16.0 TrajectoryUtils_1.16.1
## [9] SingleCellExperiment_1.30.1 SummarizedExperiment_1.38.1
## [11] Biobase_2.68.0 GenomicRanges_1.60.0
## [13] GenomeInfoDb_1.44.3 IRanges_2.42.0
## [15] S4Vectors_0.46.0 BiocGenerics_0.54.1
## [17] generics_0.1.4 MatrixGenerics_1.20.0
## [19] matrixStats_1.5.0 princurve_2.1.6
## [21] rstatix_0.7.3 stringr_1.6.0
## [23] ComplexHeatmap_2.24.1 circlize_0.4.17
## [25] ggrepel_0.9.6 CRMetrics_0.3.2
## [27] RColorBrewer_1.1-3 ggpmisc_0.6.3
## [29] ggpp_0.6.0 ggforce_0.5.0
## [31] reshape2_1.4.5 STRINGdb_2.20.0
## [33] ggpubr_0.6.2 cowplot_1.2.0
## [35] ggsci_4.2.0 ggplot2_4.0.2
## [37] qs_0.27.3 scHelper_0.0.5
## [39] sccore_1.0.5 cacoa_0.4.0
## [41] dplyr_1.2.0 magrittr_2.0.4
## [43] conos_1.5.2 igraph_2.2.1
## [45] Matrix_1.7-4
##
## loaded via a namespace (and not attached):
## [1] rTensor_1.4.9 R.methodsS3_1.8.2
## [3] dichromat_2.0-0.1 Rmisc_1.5.1
## [5] Biostrings_2.76.0 vctrs_0.7.1
## [7] ggtangle_0.1.1 RApiSerialize_0.1.4
## [9] digest_0.6.39 png_0.1-8
## [11] shape_1.4.6.1 registry_0.5-1
## [13] combinat_0.0-8 magick_2.9.0
## [15] MASS_7.3-65 foreach_1.5.2
## [17] httpuv_1.6.16 qvalue_2.40.0
## [19] withr_3.0.2 ggrastr_1.0.2
## [21] psych_2.5.6 xfun_0.56
## [23] ggfun_0.2.0 survival_3.8-6
## [25] memoise_2.0.1 ggbeeswarm_0.7.3
## [27] gson_0.1.0 clusterProfiler_4.16.0
## [29] MatrixModels_0.5-4 tidytree_0.4.7
## [31] GlobalOptions_0.1.3 gtools_3.9.5
## [33] pbapply_1.7-4 R.oo_1.27.1
## [35] Formula_1.2-5 KEGGREST_1.48.1
## [37] promises_1.5.0 otel_0.2.0
## [39] httr_1.4.7 hash_2.2.6.3
## [41] stringfish_0.18.0 rstudioapi_0.18.0
## [43] UCSC.utils_1.4.0 DOSE_4.2.0
## [45] polyclip_1.10-7 ca_0.71.1
## [47] TSCAN_1.46.0 GenomeInfoDbData_1.2.14
## [49] quadprog_1.5-8 SparseArray_1.8.1
## [51] xtable_1.8-4 doParallel_1.0.17
## [53] evaluate_1.0.5 S4Arrays_1.8.1
## [55] irlba_2.3.7 colorspace_2.1-2
## [57] polynom_1.4-1 later_1.4.5
## [59] viridis_0.6.5 ggtree_3.16.3
## [61] lattice_0.22-7 SparseM_1.84-2
## [63] triebeard_0.4.1 pillar_1.11.1
## [65] iterators_1.0.14 caTools_1.18.3
## [67] compiler_4.5.2 RSpectra_0.16-2
## [69] stringi_1.8.7 TSP_1.2.6
## [71] minqa_1.2.8 plyr_1.8.9
## [73] drat_0.2.5 crayon_1.5.3
## [75] abind_1.4-8 gridGraphics_0.5-1
## [77] chron_2.3-62 locfit_1.5-9.12
## [79] org.Hs.eg.db_3.21.0 graphlayouts_1.2.2
## [81] bit_4.6.0 fastmatch_1.1-8
## [83] tradeSeq_1.22.0 codetools_0.2-20
## [85] bslib_0.9.0 GetoptLong_1.1.0
## [87] mime_0.13 splines_4.5.2
## [89] Rcpp_1.1.1 quantreg_6.1
## [91] sparseMatrixStats_1.20.0 pagoda2_1.0.13
## [93] brew_1.0-10 N2R_1.0.3
## [95] knitr_1.51 blob_1.2.4
## [97] utf8_1.2.6 clue_0.3-66
## [99] fs_1.6.6 confintr_1.0.2
## [101] DelayedMatrixStats_1.30.0 Rdpack_2.6.4
## [103] ggsignif_0.6.4 ggplotify_0.1.3
## [105] tibble_3.3.1 sqldf_0.4-11
## [107] statmod_1.5.1 tweenr_2.0.3
## [109] pkgconfig_2.0.3 tools_4.5.2
## [111] Rook_1.2 cachem_1.1.0
## [113] rbibutils_2.3 RSQLite_2.4.5
## [115] viridisLite_0.4.2 DBI_1.2.3
## [117] fastmap_1.2.0 rmarkdown_2.30
## [119] scales_1.4.0 pbmcapply_1.5.1
## [121] ica_1.0-3 broom_1.0.11
## [123] sass_0.4.10 patchwork_1.3.2
## [125] carData_3.0-5 farver_2.1.2
## [127] reformulas_0.4.3.1 tidygraph_1.3.1
## [129] gsubfn_0.7 yaml_2.3.12
## [131] cli_3.6.5 purrr_1.2.1
## [133] lifecycle_1.0.5 uwot_0.2.4
## [135] backports_1.5.0 BiocParallel_1.42.2
## [137] gtable_0.3.6 rjson_0.2.23
## [139] parallel_4.5.2 ape_5.8-1
## [141] SnowballC_0.7.1 limma_3.64.3
## [143] jsonlite_2.0.0 edgeR_4.6.3
## [145] seriation_1.5.8 bitops_1.0-9
## [147] bit64_4.6.0-1 Rtsne_0.17
## [149] yulab.utils_0.2.3 coda.base_1.0.3
## [151] proto_1.0.0 urltools_1.7.3.1
## [153] RcppParallel_5.1.11-1 jquerylib_0.1.4
## [155] GOSemSim_2.34.0 R.utils_2.13.0
## [157] lazyeval_0.2.2 shiny_1.12.1
## [159] htmltools_0.5.9 enrichplot_1.28.4
## [161] GO.db_3.21.0 rappdirs_0.3.4
## [163] glue_1.8.0 XVector_0.48.0
## [165] treeio_1.32.0 mclust_6.1.2
## [167] dunn.test_1.3.6 mnormt_2.1.1
## [169] RMTstat_0.3.1 gridExtra_2.3
## [171] boot_1.3-32 R6_2.6.1
## [173] tidyr_1.3.2 DESeq2_1.48.2
## [175] gplots_3.3.0 labeling_0.4.3
## [177] cluster_2.1.8.1 FSA_0.10.1
## [179] aplot_0.2.9 nloptr_2.2.1
## [181] DelayedArray_0.34.1 tidyselect_1.2.1
## [183] vipor_0.4.7 plotrix_3.8-13
## [185] dendsort_0.3.4 car_3.1-3
## [187] AnnotationDbi_1.70.0 leidenAlg_1.1.5
## [189] fastICA_1.2-7 KernSmooth_2.23-26
## [191] S7_0.2.1 data.table_1.18.0
## [193] fgsea_1.34.2 rlang_1.1.7
## [195] lsa_0.73.4 ggnewscale_0.5.2
## [197] Cairo_1.7-0 beeswarm_0.4.0